2023
Conference article
Open Access
GAM Forest explanation
Lucchese C., Orlando S., Perego R., Veneri A.
Most accurate machine learning models unfortunately produce black-box predictions, for which it is impossible to grasp the internal logic that leads to a specific decision. Unfolding the logic of such black-box models is of increasing importance, especially when they are used in sensitive decision-making processes. In thisworkwe focus on forests of decision trees, which may include hundreds to thousands of decision trees to produce accurate predictions. Such complexity raises the need of developing explanations for the predictions generated by large forests.We propose a post hoc explanation method of large forests, named GAM-based Explanation of Forests (GEF), which builds a Generalized Additive Model (GAM) able to explain, both locally and globally, the impact on the predictions of a limited set of features and feature interactions.We evaluate GEF over both synthetic and real-world datasets and show that GEF can create a GAM model with high fidelity by analyzing the given forest only and without using any further information, not even the initial training dataset.Source: EDBT 2022 - 26th International Conference on Extending Database Technology, pp. 171–182, Ioannina, Greece, 28-31/03/2023
DOI: 10.48786/edbt.2023.14
Metrics:
Lucchese C., Orlando S., Perego R., Veneri A.
Most accurate machine learning models unfortunately produce black-box predictions, for which it is impossible to grasp the internal logic that leads to a specific decision. Unfolding the logic of such black-box models is of increasing importance, especially when they are used in sensitive decision-making processes. In thisworkwe focus on forests of decision trees, which may include hundreds to thousands of decision trees to produce accurate predictions. Such complexity raises the need of developing explanations for the predictions generated by large forests.We propose a post hoc explanation method of large forests, named GAM-based Explanation of Forests (GEF), which builds a Generalized Additive Model (GAM) able to explain, both locally and globally, the impact on the predictions of a limited set of features and feature interactions.We evaluate GEF over both synthetic and real-world datasets and show that GEF can create a GAM model with high fidelity by analyzing the given forest only and without using any further information, not even the initial training dataset.Source: EDBT 2022 - 26th International Conference on Extending Database Technology, pp. 171–182, Ioannina, Greece, 28-31/03/2023
DOI: 10.48786/edbt.2023.14
Metrics:
See at:
ISTI Repository 
| openproceedings.org | CNR ExploRA
2023
Conference article
Restricted
A geometric framework for query performance prediction in conversational search
Faggioli G., Ferro N., Muntean C. I., Perego R., Tonellotto N.
Thanks to recent advances in IR and NLP, the way users interact with search engines is evolving rapidly, with multi-turn conversations replacing traditional one-shot textual queries. Given its interactive nature, Conversational Search (CS) is one of the scenarios that can benefit the most from Query Performance Prediction (QPP) techniques. QPP for the CS domain is a relatively new field and lacks proper framing. In this study, we address this gap by proposing a framework for the application of QPP in the CS domain and use it to evaluate the performance of predictors. We characterize what it means to predict the performance in the CS scenario, where information needs are not independent queries but a series of closely related utterances. We identify three main ways to use QPP models in the CS domain: as a diagnostic tool, as a way to adjust the system's behaviour during a conversation, or as a way to predict the system's performance on the next utterance. Due to the lack of established evaluation procedures for QPP in the CS domain, we propose a protocol to evaluate QPPs for each of the use cases. Additionally, we introduce a set of spatial-based QPP models designed to work the best in the conversational search domain, where dense neural retrieval models are the most common approaches and query cutoffs are typically small. We show how the proposed QPP approaches improve significantly the predictive performance over the state-of-the-art in different scenarios and collections.Source: SIGIR '23 - 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1355–1365, Taipei, Taiwan, 23-27/07/2023
DOI: 10.1145/3539618.3591625
Project(s): SoBigData-PlusPlus
Metrics:
Faggioli G., Ferro N., Muntean C. I., Perego R., Tonellotto N.
Thanks to recent advances in IR and NLP, the way users interact with search engines is evolving rapidly, with multi-turn conversations replacing traditional one-shot textual queries. Given its interactive nature, Conversational Search (CS) is one of the scenarios that can benefit the most from Query Performance Prediction (QPP) techniques. QPP for the CS domain is a relatively new field and lacks proper framing. In this study, we address this gap by proposing a framework for the application of QPP in the CS domain and use it to evaluate the performance of predictors. We characterize what it means to predict the performance in the CS scenario, where information needs are not independent queries but a series of closely related utterances. We identify three main ways to use QPP models in the CS domain: as a diagnostic tool, as a way to adjust the system's behaviour during a conversation, or as a way to predict the system's performance on the next utterance. Due to the lack of established evaluation procedures for QPP in the CS domain, we propose a protocol to evaluate QPPs for each of the use cases. Additionally, we introduce a set of spatial-based QPP models designed to work the best in the conversational search domain, where dense neural retrieval models are the most common approaches and query cutoffs are typically small. We show how the proposed QPP approaches improve significantly the predictive performance over the state-of-the-art in different scenarios and collections.Source: SIGIR '23 - 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1355–1365, Taipei, Taiwan, 23-27/07/2023
DOI: 10.1145/3539618.3591625
Project(s): SoBigData-PlusPlus
Metrics:
See at:
dl.acm.org 
| CNR ExploRA
2023
Journal article
Open Access
Social search: retrieving information in online social platforms - a survey
Amendola M., Passarella A., Perego R.
Social Search research studies methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new taxonomy and highlighting current limitations and open research directions. We divide the Social Search area into three subcategories, where the social aspect plays a pivotal role: Social Question&Answering, Social Content Search, and Social Collaborative Search. For each subcategory, we present the key concepts and selected representative approaches in the literature in greater detail. We found that, up to now, a large body of studies model users' preferences and their relations by simply combining social features made available by social platforms. It paves the way for significant research to exploit more structured information about users' social profiles and behaviours (as they can be inferred from data available on social platforms) to optimize their information needs further.Source: Online social networks and media 36 (2023). doi:10.1016/j.osnem.2023.100254
DOI: 10.1016/j.osnem.2023.100254
DOI: 10.48550/arxiv.2209.14369
Project(s): SoBigData-PlusPlus
Metrics:
Amendola M., Passarella A., Perego R.
Social Search research studies methodologies exploiting social information to better satisfy user information needs in Online Social Media while simplifying the search effort and consequently reducing the time spent and the computational resources utilized. Starting from previous studies, in this work, we analyze the current state of the art of the Social Search area, proposing a new taxonomy and highlighting current limitations and open research directions. We divide the Social Search area into three subcategories, where the social aspect plays a pivotal role: Social Question&Answering, Social Content Search, and Social Collaborative Search. For each subcategory, we present the key concepts and selected representative approaches in the literature in greater detail. We found that, up to now, a large body of studies model users' preferences and their relations by simply combining social features made available by social platforms. It paves the way for significant research to exploit more structured information about users' social profiles and behaviours (as they can be inferred from data available on social platforms) to optimize their information needs further.Source: Online social networks and media 36 (2023). doi:10.1016/j.osnem.2023.100254
DOI: 10.1016/j.osnem.2023.100254
DOI: 10.48550/arxiv.2209.14369
Project(s): SoBigData-PlusPlus
Metrics:
See at:
arXiv.org e-Print Archive | Online Social Networks and Media | ISTI Repository | ISTI Repository | www.sciencedirect.com | doi.org | CNR ExploRA
2023
Conference article
Open Access
Post-Hoc selection of pareto-optimal solutions in search and recommendation
Paparella V., Anelli V. W., Nardini F. M., Perego R., Di Noia T.
Information Retrieval (IR) and Recommender Systems (RSs) tasks are moving from computing a ranking of final results based on a single metric to multi-objective problems. Solving these problems leads to a set of Pareto-optimal solutions, known as Pareto frontier, in which no objective can be further improved without hurting the others. In principle, all the points on the Pareto frontier are potential candidates to represent the best model selected with respect to the combination of two, or more, metrics. To our knowledge, there are no well-recognized strategies to decide which point should be selected on the frontier in IR and RSs. In this paper, we propose a novel, post-hoc, theoretically-justified technique, named "Population Distance from Utopia" (PDU), to identify and select the one-best Pareto-optimal solution. PDU considers fine-grained utopia points, and measures how far each point is from its utopia point, allowing to select solutions tailored to user preferences, a novel feature we call "calibration". We compare PDU against state-of-the-art strategies through extensive experiments on tasks from both IR and RS, showing that PDU combined with calibration notably impacts the solution selection.Source: CIKM '23 - 32nd ACM International Conference on Information and Knowledge Management, pp. 2013–2023, Birmingham, UK, 21-25/10/2023
DOI: 10.1145/3583780.3615010
Metrics:
Paparella V., Anelli V. W., Nardini F. M., Perego R., Di Noia T.
Information Retrieval (IR) and Recommender Systems (RSs) tasks are moving from computing a ranking of final results based on a single metric to multi-objective problems. Solving these problems leads to a set of Pareto-optimal solutions, known as Pareto frontier, in which no objective can be further improved without hurting the others. In principle, all the points on the Pareto frontier are potential candidates to represent the best model selected with respect to the combination of two, or more, metrics. To our knowledge, there are no well-recognized strategies to decide which point should be selected on the frontier in IR and RSs. In this paper, we propose a novel, post-hoc, theoretically-justified technique, named "Population Distance from Utopia" (PDU), to identify and select the one-best Pareto-optimal solution. PDU considers fine-grained utopia points, and measures how far each point is from its utopia point, allowing to select solutions tailored to user preferences, a novel feature we call "calibration". We compare PDU against state-of-the-art strategies through extensive experiments on tasks from both IR and RS, showing that PDU combined with calibration notably impacts the solution selection.Source: CIKM '23 - 32nd ACM International Conference on Information and Knowledge Management, pp. 2013–2023, Birmingham, UK, 21-25/10/2023
DOI: 10.1145/3583780.3615010
Metrics:
See at:
ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Can embeddings analysis explain large language model ranking?
Lucchese C., Minello G., Nardini F. M., Orlando S., Perego R., Veneri A.
Understanding the behavior of deep neural networks for Information Retrieval (IR) is crucial to improve trust in these effective models. Current popular approaches to diagnose the predictions made by deep neural networks are mainly based on: i) the adherence of the retrieval model to some axiomatic property of the IR system, ii) the generation of free-text explanations, or iii) feature importance attributions. In this work, we propose a novel approach that analyzes the changes of document and query embeddings in the latent space and that might explain the inner workings of IR large pre-trained language models. In particular, we focus on predicting query/document relevance, and we characterize the predictions by analyzing the topological arrangement of the embeddings in their latent space and their evolution while passing through the layers of the network. We show that there exists a link between the embedding adjustment and the predicted score, based on how tokens cluster in the embedding space. This novel approach, grounded in the query and document tokens interplay over the latent space, provides a new perspective on neural ranker explanation and a promising strategy for improving the efficiency of the models and Query Performance Prediction (QPP).Source: CIKM '23 - 32nd ACM International Conference on Information and Knowledge Management, pp. 4150–4154, Birmingham, UK, 21-25/10/2023
DOI: 10.1145/3583780.3615225
Metrics:
Lucchese C., Minello G., Nardini F. M., Orlando S., Perego R., Veneri A.
Understanding the behavior of deep neural networks for Information Retrieval (IR) is crucial to improve trust in these effective models. Current popular approaches to diagnose the predictions made by deep neural networks are mainly based on: i) the adherence of the retrieval model to some axiomatic property of the IR system, ii) the generation of free-text explanations, or iii) feature importance attributions. In this work, we propose a novel approach that analyzes the changes of document and query embeddings in the latent space and that might explain the inner workings of IR large pre-trained language models. In particular, we focus on predicting query/document relevance, and we characterize the predictions by analyzing the topological arrangement of the embeddings in their latent space and their evolution while passing through the layers of the network. We show that there exists a link between the embedding adjustment and the predicted score, based on how tokens cluster in the embedding space. This novel approach, grounded in the query and document tokens interplay over the latent space, provides a new perspective on neural ranker explanation and a promising strategy for improving the efficiency of the models and Query Performance Prediction (QPP).Source: CIKM '23 - 32nd ACM International Conference on Information and Knowledge Management, pp. 4150–4154, Birmingham, UK, 21-25/10/2023
DOI: 10.1145/3583780.3615225
Metrics:
See at:
ISTI Repository | CNR ExploRA
2023
Conference article
Open Access
Commonsense injection in conversational systems: an adaptable framework for query expansion
Rocchietti G., Frieder O., Muntean C. I., Nardini F. M., Perego R.
Recent advancements in conversational agents are leading a paradigm shift in how people search for their information needs, from text queries to entire spoken conversations. This paradigm shift poses a new challenge: a single question may lack the context driven by the entire conversation. We propose and evaluate a framework to deal with multi-turn conversations with the injection of commonsense knowledge. Specifically, we propose a novel approach for conversational search that uses pre-trained large language models and commonsense knowledge bases to enrich queries with relevant concepts. Our framework comprises a generator of candidate concepts related to the context of the conversation and a selector for deciding which candidate concept to add to the current utterance to improve retrieval effectiveness. We use the TREC CAsT datasets and ConceptNet to show that our framework improves retrieval performance by up to 82% in terms of Recall@200 and up to 154% in terms of NDCG@3 as compared to the performance achieved by the original utterances in the conversations.Source: IEEE/WAT - 22nd International Conference on Web Intelligence and Intelligent Agent Technology, pp. 48–55, Venezia, Italy, 26-29/10/2023
DOI: 10.1109/wi-iat59888.2023.00013
Metrics:
Rocchietti G., Frieder O., Muntean C. I., Nardini F. M., Perego R.
Recent advancements in conversational agents are leading a paradigm shift in how people search for their information needs, from text queries to entire spoken conversations. This paradigm shift poses a new challenge: a single question may lack the context driven by the entire conversation. We propose and evaluate a framework to deal with multi-turn conversations with the injection of commonsense knowledge. Specifically, we propose a novel approach for conversational search that uses pre-trained large language models and commonsense knowledge bases to enrich queries with relevant concepts. Our framework comprises a generator of candidate concepts related to the context of the conversation and a selector for deciding which candidate concept to add to the current utterance to improve retrieval effectiveness. We use the TREC CAsT datasets and ConceptNet to show that our framework improves retrieval performance by up to 82% in terms of Recall@200 and up to 154% in terms of NDCG@3 as compared to the performance achieved by the original utterances in the conversations.Source: IEEE/WAT - 22nd International Conference on Web Intelligence and Intelligent Agent Technology, pp. 48–55, Venezia, Italy, 26-29/10/2023
DOI: 10.1109/wi-iat59888.2023.00013
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2023
Conference article
Open Access
Rewriting conversational utterances with instructed large language models
Galimzhanova E., Muntean C. I., Nardini F. M., Perego R., Rocchietti G.
Many recent studies have shown the ability of large language models (LLMs) to achieve state-of-the-art performance on many NLP tasks, such as question answering, text summarization, coding, and translation. In some cases, the results provided by LLMs are on par with those of human experts. These models' most disruptive innovation is their ability to perform tasks via zero-shot or few-shot prompting. This capability has been successfully exploited to train instructed LLMs, where reinforcement learning with human feedback is used to guide the model to follow the user's requests directly. In this paper, we investigate the ability of instructed LLMs to improve conversational search effectiveness by rewriting user questions in a conversational setting. We study which prompts provide the most informative rewritten utterances that lead to the best retrieval performance. Reproducible experiments are conducted on publicly-available TREC CAST datasets. The results show that rewriting conversational utterances with instructed LLMs achieves significant improvements of up to 25.2% in MRR, 31.7% in Precision@1, 27% in NDCG@3, and 11.5% in Recall@500 over state-of-the-art techniques.Source: IEEE/WAT - 22nd International Conference on Web Intelligence and Intelligent Agent Technology, pp. 56–63, Venezia, Italy, 26-29/10/2023
DOI: 10.1109/wi-iat59888.2023.00014
Metrics:
Galimzhanova E., Muntean C. I., Nardini F. M., Perego R., Rocchietti G.
Many recent studies have shown the ability of large language models (LLMs) to achieve state-of-the-art performance on many NLP tasks, such as question answering, text summarization, coding, and translation. In some cases, the results provided by LLMs are on par with those of human experts. These models' most disruptive innovation is their ability to perform tasks via zero-shot or few-shot prompting. This capability has been successfully exploited to train instructed LLMs, where reinforcement learning with human feedback is used to guide the model to follow the user's requests directly. In this paper, we investigate the ability of instructed LLMs to improve conversational search effectiveness by rewriting user questions in a conversational setting. We study which prompts provide the most informative rewritten utterances that lead to the best retrieval performance. Reproducible experiments are conducted on publicly-available TREC CAST datasets. The results show that rewriting conversational utterances with instructed LLMs achieves significant improvements of up to 25.2% in MRR, 31.7% in Precision@1, 27% in NDCG@3, and 11.5% in Recall@500 over state-of-the-art techniques.Source: IEEE/WAT - 22nd International Conference on Web Intelligence and Intelligent Agent Technology, pp. 56–63, Venezia, Italy, 26-29/10/2023
DOI: 10.1109/wi-iat59888.2023.00014
Metrics:
See at:
ISTI Repository | ieeexplore.ieee.org | CNR ExploRA
2023
Journal article
Open Access
Early exit strategies for learning-to-rank cascades
Busolin F., Lucchese C., Nardini F. M., Orlando S., Perego R., Trani S.
The ranking pipelines of modern search platforms commonly exploit complex machine-learned models and have a significant impact on the query response time. In this paper, we discuss several techniques to speed up the document scoring process based on large ensembles of decision trees without hindering ranking quality. Specifically, we study the problem of document early exit within the framework of a cascading ranker made of three components: 1) an efficient but sub-optimal ranking stage; 2) a pruner that exploits signals from the previous component to force the early exit of documents classified as not relevant; and 3) a final high-quality component aimed at finely ranking the documents that survived the previous phase. To maximize speedup and preserve effectiveness, we aim to increase the accuracy of the pruner in identifying non-relevant documents without early exiting documents that are likely to be ranked among the final top-k results. We propose an in-depth study of heuristic and machine-learning techniques for designing the pruner. While the heuristic technique only exploits the score/ranking information supplied by the first sub-optimal ranker, the machine-learned solution named LEAR uses these signals as additional features along with those representing query-document pairs. Moreover, we study alternative solutions to implement the first ranker, either a small prefix of the original forest or an auxiliary machine-learned ranker explicitly trained for this purpose. We evaluated our techniques through reproducible experiments using publicly available datasets and state-of-the-art competitors. The experiments confirm that our early-exit strategies achieve speedups ranging from 3× to 10× without statistically significant differences in effectiveness.Source: IEEE access 11 (2023): 126691–126704. doi:10.1109/ACCESS.2023.3331088
DOI: 10.1109/access.2023.3331088
Metrics:
Busolin F., Lucchese C., Nardini F. M., Orlando S., Perego R., Trani S.
The ranking pipelines of modern search platforms commonly exploit complex machine-learned models and have a significant impact on the query response time. In this paper, we discuss several techniques to speed up the document scoring process based on large ensembles of decision trees without hindering ranking quality. Specifically, we study the problem of document early exit within the framework of a cascading ranker made of three components: 1) an efficient but sub-optimal ranking stage; 2) a pruner that exploits signals from the previous component to force the early exit of documents classified as not relevant; and 3) a final high-quality component aimed at finely ranking the documents that survived the previous phase. To maximize speedup and preserve effectiveness, we aim to increase the accuracy of the pruner in identifying non-relevant documents without early exiting documents that are likely to be ranked among the final top-k results. We propose an in-depth study of heuristic and machine-learning techniques for designing the pruner. While the heuristic technique only exploits the score/ranking information supplied by the first sub-optimal ranker, the machine-learned solution named LEAR uses these signals as additional features along with those representing query-document pairs. Moreover, we study alternative solutions to implement the first ranker, either a small prefix of the original forest or an auxiliary machine-learned ranker explicitly trained for this purpose. We evaluated our techniques through reproducible experiments using publicly available datasets and state-of-the-art competitors. The experiments confirm that our early-exit strategies achieve speedups ranging from 3× to 10× without statistically significant differences in effectiveness.Source: IEEE access 11 (2023): 126691–126704. doi:10.1109/ACCESS.2023.3331088
DOI: 10.1109/access.2023.3331088
Metrics:
See at: CNR ExploRA
2023
Conference article
Open Access
SE-PEF: a resource for personalized expert finding
Kasela P., Pasi G., Perego R.
The problem of personalization in Information Retrieval has been under study for a long time. A well-known issue related to this task is the lack of publicly available datasets to support a comparative evaluation of personalized search systems. To contribute in this respect, this paper introduces SE-PEF (StackExchange - Personalized Expert Finding), a resource useful for designing and evaluating personalized models related to the Expert Finding (EF) task. The contributed dataset includes more than 250k queries and 565k answers from 3 306 experts, which are annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. The results of the preliminary experiments conducted show the appropriateness of SE-PEF to evaluate and to train effective EF models.Source: SIGIR-AP '23: Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, pp. 288–309, Beijing, China, 26-28/11/2023
DOI: 10.1145/3624918.3625335
Metrics:
Kasela P., Pasi G., Perego R.
The problem of personalization in Information Retrieval has been under study for a long time. A well-known issue related to this task is the lack of publicly available datasets to support a comparative evaluation of personalized search systems. To contribute in this respect, this paper introduces SE-PEF (StackExchange - Personalized Expert Finding), a resource useful for designing and evaluating personalized models related to the Expert Finding (EF) task. The contributed dataset includes more than 250k queries and 565k answers from 3 306 experts, which are annotated with a rich set of features modeling the social interactions among the users of a popular cQA platform. The results of the preliminary experiments conducted show the appropriateness of SE-PEF to evaluate and to train effective EF models.Source: SIGIR-AP '23: Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region, pp. 288–309, Beijing, China, 26-28/11/2023
DOI: 10.1145/3624918.3625335
Metrics:
See at:
dl.acm.org | ISTI Repository | CNR ExploRA
2023
Contribution to conference
Restricted
A spatial approach to predict performance of conversational search systems
Faggioli G., Ferro N., Muntean C., Perego R., Tonellotto N.
Recent advancements in Information Retrieval and Natural Language Processing have led to significant developments in the way users interact with search engines, with traditional one-shot textual queries being replaced by multi-turn conversations. As a highly interactive search scenario, Conversational Search (CS) can significantly benefit from Query Performance Prediction (QPP) techniques. However, the application of QPP in the CS domain is a relatively new field and requires proper framing. This study proposes a set of spatial-based QPP models, designed to work effectively in the conversational search domain, where dense neural retrieval models are the most common approach and query cutoffs are small. The proposed QPP approaches are shown to improve the predictive performance over the state-of-the-art in different scenarios and collections, highlighting the utility of QPP in the CS domain.Source: IIR2023 - 13th Italian Information Retrieval Workshop, pp. 41–46, Pisa, Italy, 8-9/06/2023
Faggioli G., Ferro N., Muntean C., Perego R., Tonellotto N.
Recent advancements in Information Retrieval and Natural Language Processing have led to significant developments in the way users interact with search engines, with traditional one-shot textual queries being replaced by multi-turn conversations. As a highly interactive search scenario, Conversational Search (CS) can significantly benefit from Query Performance Prediction (QPP) techniques. However, the application of QPP in the CS domain is a relatively new field and requires proper framing. This study proposes a set of spatial-based QPP models, designed to work effectively in the conversational search domain, where dense neural retrieval models are the most common approach and query cutoffs are small. The proposed QPP approaches are shown to improve the predictive performance over the state-of-the-art in different scenarios and collections, highlighting the utility of QPP in the CS domain.Source: IIR2023 - 13th Italian Information Retrieval Workshop, pp. 41–46, Pisa, Italy, 8-9/06/2023
See at:
ceur-ws.org | CNR ExploRA
2023
Journal article
Open Access
Social search: Retrieving information in Online Social platforms - A suvey
Amendola M., Passarella A., Perego R.
Social Search research studies methodologies exploiting social information tobetter satisfy user information needs in Online Social Media while simplifyingthe search effort and consequently reducing the time spent and the computationalresources utilized. Starting from previous studies, in this work, weanalyze the current state of the art of the Social Search area, proposing a newtaxonomy and highlighting current limitations and open research directions.We divide the Social Search area into three subcategories, where the social aspectplays a pivotal role: Social Question&Answering, Social Content Search,and Social Collaborative Search. For each subcategory, we present the keyconcepts and selected representative approaches in the literature in greaterdetail. We found that, up to now, a large body of studies model users' preferencesand their relations by simply combining social features made availableby social platforms. It paves the way for significant research to exploit morestructured information about users' social profiles and behaviours (as theycan be inferred from data available on social platforms) to optimize theirinformation needs further.Source: Online social networks and media 36 (2023). doi:10.1016/j.osnem.2023.100254
DOI: 10.1016/j.osnem.2023.100254
DOI: 10.48550/arxiv.2209.14369
Metrics:
Amendola M., Passarella A., Perego R.
Social Search research studies methodologies exploiting social information tobetter satisfy user information needs in Online Social Media while simplifyingthe search effort and consequently reducing the time spent and the computationalresources utilized. Starting from previous studies, in this work, weanalyze the current state of the art of the Social Search area, proposing a newtaxonomy and highlighting current limitations and open research directions.We divide the Social Search area into three subcategories, where the social aspectplays a pivotal role: Social Question&Answering, Social Content Search,and Social Collaborative Search. For each subcategory, we present the keyconcepts and selected representative approaches in the literature in greaterdetail. We found that, up to now, a large body of studies model users' preferencesand their relations by simply combining social features made availableby social platforms. It paves the way for significant research to exploit morestructured information about users' social profiles and behaviours (as theycan be inferred from data available on social platforms) to optimize theirinformation needs further.Source: Online social networks and media 36 (2023). doi:10.1016/j.osnem.2023.100254
DOI: 10.1016/j.osnem.2023.100254
DOI: 10.48550/arxiv.2209.14369
Metrics:
See at:
arXiv.org e-Print Archive | Online Social Networks and Media | ISTI Repository | www.sciencedirect.com | doi.org | CNR ExploRA
2023
Conference article
Open Access
TrajParquet: a trajectory-oriented column file format for mobility data lakes
Koutroumanis N., Doulkeridis C., Renso C., Nanni M., Perego R.
Columnar data formats, such as Apache Parquet, are increasingly popular nowadays for scalable data storage and querying data lakes, due to compressed storage and efficient data access via data skipping. However, when applied to spatial or spatio-temporal data, advanced solutions are required to go beyond pruning over single attributes and towards multidimensional pruning. Even though there exist solutions for geospatial data, such as GeoParquet and SpatialParquet, they fall short when applied to trajectory data (sequences of spatio-temporal positions). In this paper, we propose TrajParquet, a format for columnar storage of trajectory data, which is highly efficient and scalable. Also, we present a query processing algorithm that supports spatio-temporal range queries over TrajParquet. We evaluate TrajParquet using real-world data sets and in comparison with extensions of GeoParquet and SpatialParquet, suitable for handling spatio-temporal data.Source: SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, pp. 73:1–73:4, 13-16/11/2023
DOI: 10.1145/3589132.3625623
Project(s): SoBigData-PlusPlus
Metrics:
Koutroumanis N., Doulkeridis C., Renso C., Nanni M., Perego R.
Columnar data formats, such as Apache Parquet, are increasingly popular nowadays for scalable data storage and querying data lakes, due to compressed storage and efficient data access via data skipping. However, when applied to spatial or spatio-temporal data, advanced solutions are required to go beyond pruning over single attributes and towards multidimensional pruning. Even though there exist solutions for geospatial data, such as GeoParquet and SpatialParquet, they fall short when applied to trajectory data (sequences of spatio-temporal positions). In this paper, we propose TrajParquet, a format for columnar storage of trajectory data, which is highly efficient and scalable. Also, we present a query processing algorithm that supports spatio-temporal range queries over TrajParquet. We evaluate TrajParquet using real-world data sets and in comparison with extensions of GeoParquet and SpatialParquet, suitable for handling spatio-temporal data.Source: SIGSPATIAL '23 - 31st ACM International Conference on Advances in Geographic Information Systems, pp. 73:1–73:4, 13-16/11/2023
DOI: 10.1145/3589132.3625623
Project(s): SoBigData-PlusPlus
Metrics:
See at:
ISTI Repository | CNR ExploRA
2022
Journal article
Open Access
Dynamic hard pruning of Neural Networks at the edge of the internet
Valerio L., Nardini F. M., Passarella A., Perego R.
Neural Networks (NN), although successfully applied to several Artificial Intelligence tasks, are often unnecessarily over-parametrized. In edge/fog computing, this might make their training prohibitive on resource-constrained devices, contrasting with the current trend of decentralizing intelligence from remote data centres to local constrained devices. Therefore, we investigate the problem of training effective NN models on constrained devices having a fixed, potentially small, memory budget. We target techniques that are both resource-efficient and performance effective while enabling significant network compression. Our Dynamic Hard Pruning (DynHP) technique incrementally prunes the network during training, identifying neurons that marginally contribute to the model accuracy. DynHP enables a tunable size reduction of the final neural network and reduces the NN memory occupancy during training. Freed memory is reused by a dynamic batch sizing approach to counterbalance the accuracy degradation caused by the hard pruning strategy, improving its convergence and effectiveness. We assess the performance of DynHP through reproducible experiments on three public datasets, comparing them against reference competitors. Results show that DynHP compresses a NN up to 10 times without significant performance drops (up to 3.5% additional error w.r.t. the competitors), reducing up to 80% the training memory occupancy.Source: Journal of network and computer applications 200 (2022). doi:10.1016/j.jnca.2021.103330
DOI: 10.1016/j.jnca.2021.103330
Metrics:
Valerio L., Nardini F. M., Passarella A., Perego R.
Neural Networks (NN), although successfully applied to several Artificial Intelligence tasks, are often unnecessarily over-parametrized. In edge/fog computing, this might make their training prohibitive on resource-constrained devices, contrasting with the current trend of decentralizing intelligence from remote data centres to local constrained devices. Therefore, we investigate the problem of training effective NN models on constrained devices having a fixed, potentially small, memory budget. We target techniques that are both resource-efficient and performance effective while enabling significant network compression. Our Dynamic Hard Pruning (DynHP) technique incrementally prunes the network during training, identifying neurons that marginally contribute to the model accuracy. DynHP enables a tunable size reduction of the final neural network and reduces the NN memory occupancy during training. Freed memory is reused by a dynamic batch sizing approach to counterbalance the accuracy degradation caused by the hard pruning strategy, improving its convergence and effectiveness. We assess the performance of DynHP through reproducible experiments on three public datasets, comparing them against reference competitors. Results show that DynHP compresses a NN up to 10 times without significant performance drops (up to 3.5% additional error w.r.t. the competitors), reducing up to 80% the training memory occupancy.Source: Journal of network and computer applications 200 (2022). doi:10.1016/j.jnca.2021.103330
DOI: 10.1016/j.jnca.2021.103330
Metrics:
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ISTI Repository | www.sciencedirect.com | CNR ExploRA
2022
Journal article
Open Access
MAT-Index: an index for fast multiple aspect trajectory similarity measuring
De Souza A. P. R., Renso C., Perego R., Bogorny V.
The semantic enrichment of mobility data with several information sources has led to a new type of movement data, the so-called multiple aspect trajectories. Comparing multiple aspect trajectories is crucial for several analysis tasks such as querying, clustering, similarity, and classification. Multiple aspect trajectory similarity measurement is more complex and computationally expensive, because of the large number and heterogeneous aspects of space, time, and semantics that require a different treatment. Only a few works in the literature focus on optimizing all these dimensions in a single solution, and, to the best of our knowledge, none of them proposes a fast point-to-point comparison. In this article we propose the Multiple Aspect Trajectory Index, an index data structure for optimizing the point-to-point comparison of multiple aspect trajectories, considering its three basic dimensions of space, time, and semantics. Quantitative and qualitative evaluations show a processing time reduction of up to 98.1%.Source: Transactions in GIS (Print) (2022). doi:10.1111/tgis.12889
DOI: 10.1111/tgis.12889
Project(s): MASTER
Metrics:
De Souza A. P. R., Renso C., Perego R., Bogorny V.
The semantic enrichment of mobility data with several information sources has led to a new type of movement data, the so-called multiple aspect trajectories. Comparing multiple aspect trajectories is crucial for several analysis tasks such as querying, clustering, similarity, and classification. Multiple aspect trajectory similarity measurement is more complex and computationally expensive, because of the large number and heterogeneous aspects of space, time, and semantics that require a different treatment. Only a few works in the literature focus on optimizing all these dimensions in a single solution, and, to the best of our knowledge, none of them proposes a fast point-to-point comparison. In this article we propose the Multiple Aspect Trajectory Index, an index data structure for optimizing the point-to-point comparison of multiple aspect trajectories, considering its three basic dimensions of space, time, and semantics. Quantitative and qualitative evaluations show a processing time reduction of up to 98.1%.Source: Transactions in GIS (Print) (2022). doi:10.1111/tgis.12889
DOI: 10.1111/tgis.12889
Project(s): MASTER
Metrics:
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onlinelibrary.wiley.com | ISTI Repository | CNR ExploRA
2022
Conference article
Closed Access
Ensemble model compression for fast and energy-efficient ranking on FPGAs
Gil-Costa V., Loor F., Molina R., Nardini F. M., Perego R., Trani S.
We investigate novel SoC-FPGA solutions for fast and energy-efficient ranking based on machine-learned ensembles of decision trees. Since the memory footprint of ranking ensembles limits the effective exploitation of programmable logic for large-scale inference tasks, we investigate binning and quantization techniques to reduce the memory occupation of the learned model and we optimize the state-of-the-art ensemble-traversal algorithm for deployment on low-cost, energy-efficient FPGA devices. The results of the experiments conducted using publicly available Learning-to-Rank datasets, show that our model compression techniques do not impact significantly the accuracy. Moreover, the reduced space requirements allow the models and the logic to be replicated on the FPGA device in order to execute several inference tasks in parallel. We discuss in details the experimental settings and the feasibility of the deployment of the proposed solution in a real setting. The results of the experiments conducted show that our FPGA solution achieves performances at the state of the art and consumes from 9 × up to 19.8 × less energy than an equivalent multi-threaded CPU implementation.Source: ECIR 2022 - 44th European Conference on IR Research, pp. 260–273, Stavanger, Norway, 10-14/04/2022
DOI: 10.1007/978-3-030-99736-6_18
Metrics:
Gil-Costa V., Loor F., Molina R., Nardini F. M., Perego R., Trani S.
We investigate novel SoC-FPGA solutions for fast and energy-efficient ranking based on machine-learned ensembles of decision trees. Since the memory footprint of ranking ensembles limits the effective exploitation of programmable logic for large-scale inference tasks, we investigate binning and quantization techniques to reduce the memory occupation of the learned model and we optimize the state-of-the-art ensemble-traversal algorithm for deployment on low-cost, energy-efficient FPGA devices. The results of the experiments conducted using publicly available Learning-to-Rank datasets, show that our model compression techniques do not impact significantly the accuracy. Moreover, the reduced space requirements allow the models and the logic to be replicated on the FPGA device in order to execute several inference tasks in parallel. We discuss in details the experimental settings and the feasibility of the deployment of the proposed solution in a real setting. The results of the experiments conducted show that our FPGA solution achieves performances at the state of the art and consumes from 9 × up to 19.8 × less energy than an equivalent multi-threaded CPU implementation.Source: ECIR 2022 - 44th European Conference on IR Research, pp. 260–273, Stavanger, Norway, 10-14/04/2022
DOI: 10.1007/978-3-030-99736-6_18
Metrics:
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doi.org | link.springer.com | CNR ExploRA
2022
Conference article
Open Access
A dependency-aware utterances permutation strategy to improve conversational evaluation
Faggioli G., Ferrante M., Ferro N., Perego R., Tonellotto N.
The rapid growth in the number and complexity of conversational agents has highlighted the need for suitable evaluation tools to describe their performance. The main evaluation paradigms move from analyzing conversations where the user explores information needs following a scripted dialogue with the agent. We argue that this is not a realistic setting: different users ask different questions (and in a diverse order), obtaining distinct answers and changing the conversation path. We analyze what happens to conversational systems performance when we change the order of the utterances in a scripted conversation while respecting temporal dependencies between them. Our results highlight that the performance of the system widely varies. Our experiments show that diverse orders of utterances determine completely different rankings of systems by performance. The current way of evaluating conversational systems is thus biased. Motivated by these observations, we propose a new evaluation approach based on dependency-aware utterance permutations to increase the power of our evaluation tools.Source: ECIR 2022 - 44th European Conference on IR Research, pp. 184–198, Stavanger, Norway, 10-14/04/2022
DOI: 10.1007/978-3-030-99736-6_13
Metrics:
Faggioli G., Ferrante M., Ferro N., Perego R., Tonellotto N.
The rapid growth in the number and complexity of conversational agents has highlighted the need for suitable evaluation tools to describe their performance. The main evaluation paradigms move from analyzing conversations where the user explores information needs following a scripted dialogue with the agent. We argue that this is not a realistic setting: different users ask different questions (and in a diverse order), obtaining distinct answers and changing the conversation path. We analyze what happens to conversational systems performance when we change the order of the utterances in a scripted conversation while respecting temporal dependencies between them. Our results highlight that the performance of the system widely varies. Our experiments show that diverse orders of utterances determine completely different rankings of systems by performance. The current way of evaluating conversational systems is thus biased. Motivated by these observations, we propose a new evaluation approach based on dependency-aware utterance permutations to increase the power of our evaluation tools.Source: ECIR 2022 - 44th European Conference on IR Research, pp. 184–198, Stavanger, Norway, 10-14/04/2022
DOI: 10.1007/978-3-030-99736-6_13
Metrics:
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ISTI Repository | doi.org | link.springer.com | CNR ExploRA
2022
Conference article
Open Access
The Istella22 dataset: bridging traditional and neural learning to rank evaluation
Dato D., Macavaney S., Nardini F. M., Perego R., Tonellotto N.
Neural approaches that use pre-trained language models are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their effectiveness compared to feature-based Learning-to-Rank (LtR) methods has not yet been well-established. A major reason for this is because present LtR benchmarks that contain query-document feature vectors do not contain the raw query and document text needed for neural models. On the other hand, the benchmarks often used for evaluating neural models, e.g., MS MARCO, TREC Robust, etc., provide text but do not provide query-document feature vectors. In this paper, we present Istella22, a new dataset that enables such comparisons by providing both query/document text and strong query-document feature vectors used by an industrial search engine. The dataset consists of a comprehensive corpus of 8.4M web documents, a collection of query-document pairs including 220 hand-crafted features, relevance judgments on a 5-graded scale, and a set of 2,198 textual queries used for testing purposes. Istella22 enables a fair evaluation of traditional learning-to-rank and transfer ranking techniques on the same data. LtR models exploit the feature-based representations of training samples while pre-trained transformer-based neural rankers can be evaluated on the corresponding textual content of queries and documents. Through preliminary experiments on Istella22, we find that neural re-ranking approaches lag behind LtR models in terms of effectiveness. However, LtR models identify the scores from neural models as strong signals.Source: SIGIR '22 - 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3099–3107, Madrid, Spain, 11-15/07/2022
DOI: 10.1145/3477495.3531740
Metrics:
Dato D., Macavaney S., Nardini F. M., Perego R., Tonellotto N.
Neural approaches that use pre-trained language models are effective at various ranking tasks, such as question answering and ad-hoc document ranking. However, their effectiveness compared to feature-based Learning-to-Rank (LtR) methods has not yet been well-established. A major reason for this is because present LtR benchmarks that contain query-document feature vectors do not contain the raw query and document text needed for neural models. On the other hand, the benchmarks often used for evaluating neural models, e.g., MS MARCO, TREC Robust, etc., provide text but do not provide query-document feature vectors. In this paper, we present Istella22, a new dataset that enables such comparisons by providing both query/document text and strong query-document feature vectors used by an industrial search engine. The dataset consists of a comprehensive corpus of 8.4M web documents, a collection of query-document pairs including 220 hand-crafted features, relevance judgments on a 5-graded scale, and a set of 2,198 textual queries used for testing purposes. Istella22 enables a fair evaluation of traditional learning-to-rank and transfer ranking techniques on the same data. LtR models exploit the feature-based representations of training samples while pre-trained transformer-based neural rankers can be evaluated on the corresponding textual content of queries and documents. Through preliminary experiments on Istella22, we find that neural re-ranking approaches lag behind LtR models in terms of effectiveness. However, LtR models identify the scores from neural models as strong signals.Source: SIGIR '22 - 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 3099–3107, Madrid, Spain, 11-15/07/2022
DOI: 10.1145/3477495.3531740
Metrics:
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ISTI Repository | dl.acm.org | CNR ExploRA
2022
Conference article
Open Access
ILMART: interpretable ranking with constrained LambdaMART
Lucchese C., Nardini F. M., Orlando S., Perego R., Veneri A.
Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on creating post-hoc explanations, in this paper we investigate how to train effective and intrinsically-interpretable ranking models. Developing these models is particularly challenging and it also requires finding a trade-off between ranking quality and model complexity. State-of-the-art rankers, made of either large ensembles of trees or several neural layers, exploit in fact an unlimited number of feature interactions making them black boxes. Previous approaches on intrinsically-interpretable ranking models address this issue by avoiding interactions between features thus paying a significant performance drop with respect to full-complexity models. Conversely, ILMART, our novel and interpretable LtR solution based on LambdaMART, is able to train effective and intelligible models by exploiting a limited and controlled number of pairwise feature interactions. Exhaustive and reproducible experiments conducted on three publicly-available LtR datasets show that ILMART outperforms the current state-of-the-art solution for interpretable ranking of a large margin with a gain of nDCG of up to 8%.Source: SIGIR '22 - 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2255–2259, Madrid, Spain, 11-15/07/2022
DOI: 10.1145/3477495.3531840
Metrics:
Lucchese C., Nardini F. M., Orlando S., Perego R., Veneri A.
Interpretable Learning to Rank (LtR) is an emerging field within the research area of explainable AI, aiming at developing intelligible and accurate predictive models. While most of the previous research efforts focus on creating post-hoc explanations, in this paper we investigate how to train effective and intrinsically-interpretable ranking models. Developing these models is particularly challenging and it also requires finding a trade-off between ranking quality and model complexity. State-of-the-art rankers, made of either large ensembles of trees or several neural layers, exploit in fact an unlimited number of feature interactions making them black boxes. Previous approaches on intrinsically-interpretable ranking models address this issue by avoiding interactions between features thus paying a significant performance drop with respect to full-complexity models. Conversely, ILMART, our novel and interpretable LtR solution based on LambdaMART, is able to train effective and intelligible models by exploiting a limited and controlled number of pairwise feature interactions. Exhaustive and reproducible experiments conducted on three publicly-available LtR datasets show that ILMART outperforms the current state-of-the-art solution for interpretable ranking of a large margin with a gain of nDCG of up to 8%.Source: SIGIR '22 - 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2255–2259, Madrid, Spain, 11-15/07/2022
DOI: 10.1145/3477495.3531840
Metrics:
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ISTI Repository | dl.acm.org | CNR ExploRA
2022
Conference article
Open Access
A federated cloud solution for transnational mobility data sharing
Carlini E., Chevalier T., Dazzi P., Lettich F., Perego R., Renso C., Trani S.
Nowadays, innovative digital services are massively spreading both in the public and private sectors. In this work we focus on the digital data regarding the mobility of persons and goods, which are experiencing exponential growth thanks to the significant diffusion of telecommunication infrastructures and inexpensive GPS-equipped devices. The volume, velocity, and heterogeneity of mobility data call for advanced and efficient services to collect and integrate various data sources from different data producers. The MobiDataLab H2020 project aims to deal with these challenges by introducing an efficient and highly interoperable digital framework for mobility data sharing. In particular, the project aims to propose to the mobility stakeholders (i.e., transport organising authorities, operators, industry, governments, and innovators) reproducible methodologies and sustainable tools that can foster the development of a data-sharing culture in Europe and beyond. This paper introduces the key concepts driving the design and definition of a cloud-based data-sharing federation we call the Transport Cloud platform, which represents one of the main pillars of the MobiDataLab project. Such platform aims to ensure transnational access to mobility data in a secure, efficient, and seamless way, and to ensure that FAIR principles (i.e., mobility data should be findable, accessible, interoperable, and reusable) are enforced.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 586–592, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): ACCORDION, MobiDataLab
Carlini E., Chevalier T., Dazzi P., Lettich F., Perego R., Renso C., Trani S.
Nowadays, innovative digital services are massively spreading both in the public and private sectors. In this work we focus on the digital data regarding the mobility of persons and goods, which are experiencing exponential growth thanks to the significant diffusion of telecommunication infrastructures and inexpensive GPS-equipped devices. The volume, velocity, and heterogeneity of mobility data call for advanced and efficient services to collect and integrate various data sources from different data producers. The MobiDataLab H2020 project aims to deal with these challenges by introducing an efficient and highly interoperable digital framework for mobility data sharing. In particular, the project aims to propose to the mobility stakeholders (i.e., transport organising authorities, operators, industry, governments, and innovators) reproducible methodologies and sustainable tools that can foster the development of a data-sharing culture in Europe and beyond. This paper introduces the key concepts driving the design and definition of a cloud-based data-sharing federation we call the Transport Cloud platform, which represents one of the main pillars of the MobiDataLab project. Such platform aims to ensure transnational access to mobility data in a secure, efficient, and seamless way, and to ensure that FAIR principles (i.e., mobility data should be findable, accessible, interoperable, and reusable) are enforced.Source: SEBD 2022 - 30th Italian Symposium on Advanced Database Systems, pp. 586–592, Tirrenia, Pisa, Italy, 19-22/06/2022
Project(s): ACCORDION
, MobiDataLab
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2022
Contribution to conference
Open Access
Energy-efficient ranking on FPGAs through ensemble model compression (Abstract)
Gil-Costa V., Loor F., Molina R., Nardini F. M., Perego R., Trani S.
In this talk, we present the main results of a paper accepted at ECIR 2022 [1]. We investigate novel SoC-FPGA solutions for fast and energy-efficient ranking based on machine learned ensembles of decision trees. Since the memory footprint of ranking ensembles limits the effective exploitation of programmable logic for large-scale inference tasks [2], we investigate binning and quantization techniques to reduce the memory occupation of the learned model and we optimize the state-of-the-art ensemble-traversal algorithm for deployment on lowcost, energy-efficient FPGA devices. The results of the experiments conducted using publicly available Learning-to-Rank datasets, show that our model compression techniques do not impact significantly the accuracy. Moreover, the reduced space requirements allow the models and the logic to be replicated on the FPGA device in order to execute several inference tasks in parallel. We discuss in details the experimental settings and the feasibility of the deployment of the proposed solution in a real setting. The results of the experiments conducted show that our FPGA solution achieves performances at the state of the art and consumes from 9× up to 19.8× less energy than an equivalent multi-threaded CPU implementation.Source: IIR 2022 - 12th Italian Information Retrieval Workshop 2022, Tirrenia, Pisa, Italy, 19-22/06/2022
Gil-Costa V., Loor F., Molina R., Nardini F. M., Perego R., Trani S.
In this talk, we present the main results of a paper accepted at ECIR 2022 [1]. We investigate novel SoC-FPGA solutions for fast and energy-efficient ranking based on machine learned ensembles of decision trees. Since the memory footprint of ranking ensembles limits the effective exploitation of programmable logic for large-scale inference tasks [2], we investigate binning and quantization techniques to reduce the memory occupation of the learned model and we optimize the state-of-the-art ensemble-traversal algorithm for deployment on lowcost, energy-efficient FPGA devices. The results of the experiments conducted using publicly available Learning-to-Rank datasets, show that our model compression techniques do not impact significantly the accuracy. Moreover, the reduced space requirements allow the models and the logic to be replicated on the FPGA device in order to execute several inference tasks in parallel. We discuss in details the experimental settings and the feasibility of the deployment of the proposed solution in a real setting. The results of the experiments conducted show that our FPGA solution achieves performances at the state of the art and consumes from 9× up to 19.8× less energy than an equivalent multi-threaded CPU implementation.Source: IIR 2022 - 12th Italian Information Retrieval Workshop 2022, Tirrenia, Pisa, Italy, 19-22/06/2022
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA