2012
Contribution to book
Restricted
Exploring the meaning behind Twitter hashtags through clustering.
Muntean C. I., Morar G. A., Moldovan D.
Social networks are generators of large amount of data produced by users, who are not limited with respect to the content of the information they exchange. The data generated can be a good indicator of trends and topic preferences among users. In our paper we focus on analyzing and representing hashtags by the corpus in which they appear. We cluster a large set of hashtags using K-means on map reduce in order to process data in a distributed manner. Our intention is to retrieve connections that might exist between different hashtags and their textual representation, and grasp their semantics through the main topics they occur with.Source: BIS 2012 - Business Information Systems Workshops. Revised papers, edited by Witold Abramowicz, John Domingue, Krzysztof W?cel, pp. 231–242. London: Springer, 2012
DOI: 10.1007/978-3-642-34228-8_22
Metrics:
Muntean C. I., Morar G. A., Moldovan D.
Social networks are generators of large amount of data produced by users, who are not limited with respect to the content of the information they exchange. The data generated can be a good indicator of trends and topic preferences among users. In our paper we focus on analyzing and representing hashtags by the corpus in which they appear. We cluster a large set of hashtags using K-means on map reduce in order to process data in a distributed manner. Our intention is to retrieve connections that might exist between different hashtags and their textual representation, and grasp their semantics through the main topics they occur with.Source: BIS 2012 - Business Information Systems Workshops. Revised papers, edited by Witold Abramowicz, John Domingue, Krzysztof W?cel, pp. 231–242. London: Springer, 2012
DOI: 10.1007/978-3-642-34228-8_22
Metrics:
See at:
doi.org 
| gateway.webofknowledge.com | link.springer.com | CNR ExploRA
2015
Conference article
Restricted
MUSETS: Diversity-aware web query suggestions for shortening user sessions
Sydow M., Muntean C. I., Nardini F. M., Matwin S., Silvestri F.
We propose MUSETS (multi-session total shortening) - a novel formulation of the query suggestion task, specified as an optimization problem. Given an ambiguous user query, the goal is to propose the user a set of query suggestions that optimizes a diversity-aware objective function. The function models the expected number of query reformulations that a user would save until reaching a satisfactory query formulation. The function is diversity-aware, as it naturally enforces high coverage of different alternative continuations of the user session. For modeling the topics covered by the queries, we also use an extended query representation based on entities extracted from Wikipedia. We apply a machine learning approach to learn the model on a set of user sessions to be subsequently used for queries that are under-represented in historical query logs and present an evaluation of the approach.Source: Foundations of Intelligent Systems. 22nd International Symposium, pp. 237–247, Lyon, France, 21-23/10/2015
DOI: 10.1007/978-3-319-25252-0_26
Metrics:
Sydow M., Muntean C. I., Nardini F. M., Matwin S., Silvestri F.
We propose MUSETS (multi-session total shortening) - a novel formulation of the query suggestion task, specified as an optimization problem. Given an ambiguous user query, the goal is to propose the user a set of query suggestions that optimizes a diversity-aware objective function. The function models the expected number of query reformulations that a user would save until reaching a satisfactory query formulation. The function is diversity-aware, as it naturally enforces high coverage of different alternative continuations of the user session. For modeling the topics covered by the queries, we also use an extended query representation based on entities extracted from Wikipedia. We apply a machine learning approach to learn the model on a set of user sessions to be subsequently used for queries that are under-represented in historical query logs and present an evaluation of the approach.Source: Foundations of Intelligent Systems. 22nd International Symposium, pp. 237–247, Lyon, France, 21-23/10/2015
DOI: 10.1007/978-3-319-25252-0_26
Metrics:
See at:
doi.org | link.springer.com | CNR ExploRA
2015
Conference article
Open Access
Gamification in information retrieval: State of the art, challenges and opportunities
Muntean C. I., Nardini F. M.
Gamification aims at applying game design principles and elements, such as points, badges, feedbacks or leader boards in non- gaming environments. An interesting goal of gamification is to combine and exploit the fun factor for targeting other aspects like achieving more accurate work, more cost effective solutions and better retention rates. The application of gamification techniques to IR tasks poses interesting research challenges. In this paper, we propose an analysis of the state of the art in this field and we summarize interesting challenges and oppor- tunities for the near future.Source: 6th Italian Information Retrieval Workshop, Cagliari, Italy, 25-26/05/2015
Muntean C. I., Nardini F. M.
Gamification aims at applying game design principles and elements, such as points, badges, feedbacks or leader boards in non- gaming environments. An interesting goal of gamification is to combine and exploit the fun factor for targeting other aspects like achieving more accurate work, more cost effective solutions and better retention rates. The application of gamification techniques to IR tasks poses interesting research challenges. In this paper, we propose an analysis of the state of the art in this field and we summarize interesting challenges and oppor- tunities for the near future.Source: 6th Italian Information Retrieval Workshop, Cagliari, Italy, 25-26/05/2015
See at:
ceur-ws.org 
| CNR ExploRA
2016
Contribution to conference
Open Access
Understanding human mobility during events in foursquare
Muntean C. I., Nardini F. M., Noulas A.
Social events can generate high influxes of people transitioning various locations in a city. They can be considered to have a considerable impact on the local economy, whether they are sport events, concerts or festivals. These events are capable of generating sudden changes in the activity landscape of a city, with the neighborhoods that host events becoming unusually busy and active compared to times of regular citizen activity. While event and anomaly detection more generally has been a topic of study in recent years, as also has been event recommendation for mobile users, progress has been slower towards building systems that are able to capture the sudden shift appropriately in this setting. In this work we exploit data from the location-based service Foursquare to study mobility during events in Chicago, and later expand our study to other cities as well. Our aim is to identify what differences emerge in terms of user mobility during events versus regular periods of human activity.Source: 7th Italian Information Retrieval Workshop, Venezia, Italy, 30-31 May 2016
Muntean C. I., Nardini F. M., Noulas A.
Social events can generate high influxes of people transitioning various locations in a city. They can be considered to have a considerable impact on the local economy, whether they are sport events, concerts or festivals. These events are capable of generating sudden changes in the activity landscape of a city, with the neighborhoods that host events becoming unusually busy and active compared to times of regular citizen activity. While event and anomaly detection more generally has been a topic of study in recent years, as also has been event recommendation for mobile users, progress has been slower towards building systems that are able to capture the sudden shift appropriately in this setting. In this work we exploit data from the location-based service Foursquare to study mobility during events in Chicago, and later expand our study to other cities as well. Our aim is to identify what differences emerge in terms of user mobility during events versus regular periods of human activity.Source: 7th Italian Information Retrieval Workshop, Venezia, Italy, 30-31 May 2016
See at:
ceur-ws.org | CNR ExploRA
2020
Journal article
Open Access
Crime and its fear in social media
Prieto Curiel R., Cresci S., Muntean C. I., Bishop S. R.
Social media posts incorporate real-time information that has, elsewhere, been exploited to predict social trends. This paper considers whether such information can be useful in relation to crime and fear of crime. A large number of tweets were collected from the 18 largest Spanish-speaking countries in Latin America, over a period of 70 days. These tweets are then classified as being crime-related or not and additional information is extracted, including the type of crime and where possible, any geo-location at a city level. From the analysis of collected data, it is established that around 15 out of every 1000 tweets have text related to a crime, or fear of crime. The frequency of tweets related to crime is then compared against the number of murders, the murder rate, or the level of fear of crime as recorded in surveys. Results show that, like mass media, such as newspapers, social media suffer from a strong bias towards violent or sexual crimes. Furthermore, social media messages are not highly correlated with crime. Thus, social media is shown not to be highly useful for detecting trends in crime itself, but what they do demonstrate is rather a reflection of the level of the fear of crime.Source: Palgrave communications 6 (2020). doi:10.1057/s41599-020-0430-7
DOI: 10.1057/s41599-020-0430-7
Project(s): CIMPLEX
, SoBigData
Metrics:
Prieto Curiel R., Cresci S., Muntean C. I., Bishop S. R.
Social media posts incorporate real-time information that has, elsewhere, been exploited to predict social trends. This paper considers whether such information can be useful in relation to crime and fear of crime. A large number of tweets were collected from the 18 largest Spanish-speaking countries in Latin America, over a period of 70 days. These tweets are then classified as being crime-related or not and additional information is extracted, including the type of crime and where possible, any geo-location at a city level. From the analysis of collected data, it is established that around 15 out of every 1000 tweets have text related to a crime, or fear of crime. The frequency of tweets related to crime is then compared against the number of murders, the murder rate, or the level of fear of crime as recorded in surveys. Results show that, like mass media, such as newspapers, social media suffer from a strong bias towards violent or sexual crimes. Furthermore, social media messages are not highly correlated with crime. Thus, social media is shown not to be highly useful for detecting trends in crime itself, but what they do demonstrate is rather a reflection of the level of the fear of crime.Source: Palgrave communications 6 (2020). doi:10.1057/s41599-020-0430-7
DOI: 10.1057/s41599-020-0430-7
Project(s): CIMPLEX
, SoBigData Metrics:
See at:
Palgrave Communications | Palgrave Communications | ISTI Repository | www.nature.com | Palgrave Communications | CNR ExploRA
2020
Conference article
Embargo
High-quality prediction of tourist movements using temporal trajectories in graphs
Moghtasedi S., Muntean C. I., Nardini F. M., Grossi R., Marino A.
In this paper, we study the problem of predicting the next position of a tourist given his history. In particular, we propose a model to identify the next point of interest that a tourist will visit in the future, by making use of similarity between trajectories on a graph and taking into account the spatial-temporal aspect of trajectories. We compare our method with a well-known machine learning-based technique, as well as with a popularity baseline, using three public real-world datasets. Our experimental results show that our technique outperforms state-of-the-art machine learning-based methods effectively, by providing at least twice more accurate results.Source: ASONAM 2020 - The 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 348–352, Online conference, 7-10/12/2020
DOI: 10.1109/asonam49781.2020.9381450
Metrics:
Moghtasedi S., Muntean C. I., Nardini F. M., Grossi R., Marino A.
In this paper, we study the problem of predicting the next position of a tourist given his history. In particular, we propose a model to identify the next point of interest that a tourist will visit in the future, by making use of similarity between trajectories on a graph and taking into account the spatial-temporal aspect of trajectories. We compare our method with a well-known machine learning-based technique, as well as with a popularity baseline, using three public real-world datasets. Our experimental results show that our technique outperforms state-of-the-art machine learning-based methods effectively, by providing at least twice more accurate results.Source: ASONAM 2020 - The 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 348–352, Online conference, 7-10/12/2020
DOI: 10.1109/asonam49781.2020.9381450
Metrics:
See at:
ieeexplore.ieee.org | xplorestaging.ieee.org | CNR ExploRA
2021
Conference article
Open Access
MICROS: Mixed-Initiative ConveRsatiOnal Systems Workshop
Mele I., Muntean C. I., Aliannejadi M., Voskarides N.
The 1st edition of the workshop on Mixed-Initiative ConveRsatiOnal Systems (MICROS@ECIR2021) aims at investigating and collecting novel ideas and contributions in the field of conversational systems. Oftentimes, the users fulfill their information need using smartphones and home assistants. This has revolutionized the way users access online information, thus posing new challenges compared to traditional search and recommendation. The first edition of MICROS will have a particular focus on mixed-initiative conversational systems. Indeed, conversational systems need to be proactive, proposing not only answers but also possible interpretations for ambiguous or vague requests.Source: ECIR 2021 - 43rd European Conference on IR Research, pp. 710–713, Online Conference, March 28 - April 1, 2021
DOI: 10.1007/978-3-030-72240-1_86
DOI: 10.48550/arxiv.2101.10219
Metrics:
Mele I., Muntean C. I., Aliannejadi M., Voskarides N.
The 1st edition of the workshop on Mixed-Initiative ConveRsatiOnal Systems (MICROS@ECIR2021) aims at investigating and collecting novel ideas and contributions in the field of conversational systems. Oftentimes, the users fulfill their information need using smartphones and home assistants. This has revolutionized the way users access online information, thus posing new challenges compared to traditional search and recommendation. The first edition of MICROS will have a particular focus on mixed-initiative conversational systems. Indeed, conversational systems need to be proactive, proposing not only answers but also possible interpretations for ambiguous or vague requests.Source: ECIR 2021 - 43rd European Conference on IR Research, pp. 710–713, Online Conference, March 28 - April 1, 2021
DOI: 10.1007/978-3-030-72240-1_86
DOI: 10.48550/arxiv.2101.10219
Metrics:
See at:
arXiv.org e-Print Archive | arxiv.org | ISTI Repository | doi.org | doi.org | link.springer.com | CNR ExploRA
2022
Conference article
Open Access
The 2nd workshop on Mixed-Initiative ConveRsatiOnal Systems (MICROS)
Mele I., Muntean C. I., Aliannejadi M., Voskarides N.
The Mixed-Initiative ConveRsatiOnal Systems workshop (MICROS) aims at bringing novel ideas and investigating new solutions on conversational assistant systems. The increasing popularity of personal assistant systems, as well as smartphones, has changed the way users access online information, posing new challenges for information seeking and filtering. MICROS has a particular focus on mixed-initiative conversational systems, namely, systems that can provide answers in a proactive way (e.g., asking for clarification or proposing possible interpretations for ambiguous and vague requests). We invite people working on conversational systems or interested in the workshop topics to send us their position and research manuscripts.Source: CIKM '22 - 31st ACM International Conference on Information & Knowledge Management, pp. 5173–5174, Atlanta, USA, 17-21/10/2022
DOI: 10.1145/3511808.3557938
Metrics:
Mele I., Muntean C. I., Aliannejadi M., Voskarides N.
The Mixed-Initiative ConveRsatiOnal Systems workshop (MICROS) aims at bringing novel ideas and investigating new solutions on conversational assistant systems. The increasing popularity of personal assistant systems, as well as smartphones, has changed the way users access online information, posing new challenges for information seeking and filtering. MICROS has a particular focus on mixed-initiative conversational systems, namely, systems that can provide answers in a proactive way (e.g., asking for clarification or proposing possible interpretations for ambiguous and vague requests). We invite people working on conversational systems or interested in the workshop topics to send us their position and research manuscripts.Source: CIKM '22 - 31st ACM International Conference on Information & Knowledge Management, pp. 5173–5174, Atlanta, USA, 17-21/10/2022
DOI: 10.1145/3511808.3557938
Metrics:
See at:
ISTI Repository | dl.acm.org | doi.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
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
2012
Conference article
Restricted
RecTour: a recommender system for tourists
Baraglia R., Frattari C., Muntean C. I., Nardini F. M., Silvestri F.
This paper presents a recommender system that provides personalized information about locations of potential interest to a tourist. The system generates suggestions, consisting of touristic places, according to the current position and history data describing the tourist movements. For the selection of tourist sites, the system uses a set of points of interest a priori identified. We evaluate our system on two datasets: a real and a synthetic one, both storing trajectories describing previous movements of tourists. The proposed solution has high applicability and the results show that the solution is both efficient and viable.Source: International Workshop on Tourism Facilities, Macau, China, 4 December 2012
DOI: 10.1109/wi-iat.2012.88
Metrics:
Baraglia R., Frattari C., Muntean C. I., Nardini F. M., Silvestri F.
This paper presents a recommender system that provides personalized information about locations of potential interest to a tourist. The system generates suggestions, consisting of touristic places, according to the current position and history data describing the tourist movements. For the selection of tourist sites, the system uses a set of points of interest a priori identified. We evaluate our system on two datasets: a real and a synthetic one, both storing trajectories describing previous movements of tourists. The proposed solution has high applicability and the results show that the solution is both efficient and viable.Source: International Workshop on Tourism Facilities, Macau, China, 4 December 2012
DOI: 10.1109/wi-iat.2012.88
Metrics:
See at:
doi.org | ieeexplore.ieee.org | CNR ExploRA
2013
Contribution to conference
Open Access
Learning to shorten query sessions.
Muntean C., Nardini F. M., Silvestri F., Sydow M.
We propose the use of learning to rank techniques to shorten query sessions by maximizing the probability that the query we predict is the final query of the current search session. We present a preliminary evaluation showing that this approach is a promising research direction.Source: WWW '13 - 22nd international Conference on World Wide Web Companion, pp. 131–132, Rio de Janeiro, Brasil, 13-17 Maggio 2013
Muntean C., Nardini F. M., Silvestri F., Sydow M.
We propose the use of learning to rank techniques to shorten query sessions by maximizing the probability that the query we predict is the final query of the current search session. We present a preliminary evaluation showing that this approach is a promising research direction.Source: WWW '13 - 22nd international Conference on World Wide Web Companion, pp. 131–132, Rio de Janeiro, Brasil, 13-17 Maggio 2013
See at:
dl.acm.org | CNR ExploRA
2012
Conference article
Open Access
A trajectory-based recommender system for tourism.
Baraglia R., Frattari C., Muntean C. I., Nardini F. M., Silvestri F.
Recommendation systems provide focused information to users on a set of objects belonging to a specific domain. The proposed recommender system provides personalized suggestions about touristic points of interest. The system generates recommendations, consisting of touristic places, according to the current position of a tourist and previously collected data describing tourist movements in a touristic location/city. The touristic sites correspond to a set of points of interest identified a priori. We propose several metrics to evaluate both the spatial coverage of the dataset and the quality of recommendations produced. We assess our system on two datasets: a real and a synthetic one. Results show that our solution is a viable one.Source: Active Media Technology. 8th International Conference, pp. 196–205, Macau, China, 4-7 December 2012
DOI: 10.1007/978-3-642-35236-2_20
Metrics:
Baraglia R., Frattari C., Muntean C. I., Nardini F. M., Silvestri F.
Recommendation systems provide focused information to users on a set of objects belonging to a specific domain. The proposed recommender system provides personalized suggestions about touristic points of interest. The system generates recommendations, consisting of touristic places, according to the current position of a tourist and previously collected data describing tourist movements in a touristic location/city. The touristic sites correspond to a set of points of interest identified a priori. We propose several metrics to evaluate both the spatial coverage of the dataset and the quality of recommendations produced. We assess our system on two datasets: a real and a synthetic one. Results show that our solution is a viable one.Source: Active Media Technology. 8th International Conference, pp. 196–205, Macau, China, 4-7 December 2012
DOI: 10.1007/978-3-642-35236-2_20
Metrics:
See at:
hpc.isti.cnr.it | doi.org | link.springer.com | CNR ExploRA
2013
Conference article
Restricted
LearNext: learning to predict tourists movements
Baraglia R., Muntean C. I., Nardini F. M., Silvestri F.
In this paper, we tackle the problem of predicting the ``next'' geographical position of a tourist given her history (i.e., the prediction is done accordingly to the tourist's current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68 dimension feature vector, specifically designed for tourism related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-the-art in touristic recommender and trail prediction systems as well as a strong popularity baseline. Experiments show that the methods we propose outperform important competitors and baselines thus providing strong evidence of the performance of our solutions.Source: CIKM '2013 - 22nd ACM International Conference on Information & Knowledge Management, pp. 751–756, San Francisco, USA, 27 October - 1 November 2013 2013
DOI: 10.1145/2505515.2505656
Metrics:
Baraglia R., Muntean C. I., Nardini F. M., Silvestri F.
In this paper, we tackle the problem of predicting the ``next'' geographical position of a tourist given her history (i.e., the prediction is done accordingly to the tourist's current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68 dimension feature vector, specifically designed for tourism related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-the-art in touristic recommender and trail prediction systems as well as a strong popularity baseline. Experiments show that the methods we propose outperform important competitors and baselines thus providing strong evidence of the performance of our solutions.Source: CIKM '2013 - 22nd ACM International Conference on Information & Knowledge Management, pp. 751–756, San Francisco, USA, 27 October - 1 November 2013 2013
DOI: 10.1145/2505515.2505656
Metrics:
See at:
dl.acm.org | doi.org | www.scopus.com | CNR ExploRA
2014
Contribution to conference
Open Access
LearNext: learning to predict tourists movements
Baraglia R., Muntean C. I., Nardini F. M., Silvestri F.
In this paper, we tackle the problem of predicting the "next" geographical position of a tourist given her history (i.e., the prediction is done accordingly to the tourist's current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Rank- ing SVM. The learning is done on the basis of an object space represented by a 68 dimension feature vector, specifically designed for tourism related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-the-art in touristic recommender and trail prediction systems as well as a strong popularity baseline. Experiments show that the methods we propose outperform important competitors and baselines thus providing strong evidence of the performance of our solutions.Source: 5th Italian Information Retrieval Workshop, pp. 75–79, University of Roma Tor Vergata, 21-22 January 2014
Baraglia R., Muntean C. I., Nardini F. M., Silvestri F.
In this paper, we tackle the problem of predicting the "next" geographical position of a tourist given her history (i.e., the prediction is done accordingly to the tourist's current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Rank- ing SVM. The learning is done on the basis of an object space represented by a 68 dimension feature vector, specifically designed for tourism related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-the-art in touristic recommender and trail prediction systems as well as a strong popularity baseline. Experiments show that the methods we propose outperform important competitors and baselines thus providing strong evidence of the performance of our solutions.Source: 5th Italian Information Retrieval Workshop, pp. 75–79, University of Roma Tor Vergata, 21-22 January 2014
See at:
ceur-ws.org | CNR ExploRA
2015
Journal article
Open Access
On learning prediction models for tourists paths
Muntean C. I., Nardini F. M., Silvestri F., Baraglia R.
In this article, we tackle the problem of predicting the "next" geographical position of a tourist, given her history (i.e., the prediction is done accordingly to the tourist's current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68-dimension feature vector specifically designed for tourism-related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-theart in recommender and trail prediction systems for tourism, as well as a popularity baseline. Experiments show that the methods we propose consistently outperform the baselines and provide strong evidence of the performance and robustness of our solutions.Source: ACM transactions on intelligent systems and technology (Print) 7 (2015): 8–35. doi:10.1145/2766459
DOI: 10.1145/2766459
Metrics:
Muntean C. I., Nardini F. M., Silvestri F., Baraglia R.
In this article, we tackle the problem of predicting the "next" geographical position of a tourist, given her history (i.e., the prediction is done accordingly to the tourist's current trail) by means of supervised learning techniques, namely Gradient Boosted Regression Trees and Ranking SVM. The learning is done on the basis of an object space represented by a 68-dimension feature vector specifically designed for tourism-related data. Furthermore, we propose a thorough comparison of several methods that are considered state-of-theart in recommender and trail prediction systems for tourism, as well as a popularity baseline. Experiments show that the methods we propose consistently outperform the baselines and provide strong evidence of the performance and robustness of our solutions.Source: ACM transactions on intelligent systems and technology (Print) 7 (2015): 8–35. doi:10.1145/2766459
DOI: 10.1145/2766459
Metrics:
See at:
ISTI Repository | dl.acm.org | ACM Transactions on Intelligent Systems and Technology | CNR ExploRA
2018
Report
Open Access
BASMATI - D3.5 Server- and Client-side Applications Adaptation and Reconfiguration: Design and Specification
Dazzi P., Carlini E., De Lira V. M., Munteanu C.
This report provides a description of the mechanisms, tools, and algorithms used to support application adaptation and reconfiguration in the BASMATI brokerage platform. At the core of this support lies the BASMATI Enriched Application Model (BEAM), which is the xml-based language in which an application is modelled and represented in BASMATI. The design principles behind the BEAM (namel: compatibility, extensibility, decomposability) are the prerequisites to provide efficient and effective geo-placement of services and applications on top of federated Cloud resources. The BEAM is made available to all the components of the platform by the Application Repository, which works as a centralization point for the BEAMs of all the applications. The decomposability of BEAM is exploited by the Decision Maker that has the task to proactively and reactively adapt the application according to the behaviour of users and resources, by means of advanced placement algorithms.Source: Project report, BASMATI, Deliverable D3.5, 2018
Project(s): BASMATI
Dazzi P., Carlini E., De Lira V. M., Munteanu C.
This report provides a description of the mechanisms, tools, and algorithms used to support application adaptation and reconfiguration in the BASMATI brokerage platform. At the core of this support lies the BASMATI Enriched Application Model (BEAM), which is the xml-based language in which an application is modelled and represented in BASMATI. The design principles behind the BEAM (namel: compatibility, extensibility, decomposability) are the prerequisites to provide efficient and effective geo-placement of services and applications on top of federated Cloud resources. The BEAM is made available to all the components of the platform by the Application Repository, which works as a centralization point for the BEAMs of all the applications. The decomposability of BEAM is exploited by the Decision Maker that has the task to proactively and reactively adapt the application according to the behaviour of users and resources, by means of advanced placement algorithms.Source: Project report, BASMATI, Deliverable D3.5, 2018
Project(s): BASMATI
See at:
ISTI Repository | CNR ExploRA
2020
Conference article
Open Access
Topic propagation in conversational search
Mele I., Muntean C. I., Nardini F. M., Perego R., Tonellotto N., Frieder O.
In a conversational context, a user expresses her multi-faceted information need as a sequence of natural-language questions, i.e., utterances. Starting from a given topic, the conversation evolves through user utterances and system replies. The retrieval of documents relevant to a given utterance in a conversation is challenging due to ambiguity of natural language and to the difficulty of detecting possible topic shifts and semantic relationships among utterances. We adopt the 2019 TREC Conversational Assistant Track (CAsT) framework to experiment with a modular architecture performing: (i) topic-aware utterance rewriting, (ii) retrieval of candidate passages for the rewritten utterances, and (iii) neural-based re-ranking of candidate passages. We present a comprehensive experimental evaluation of the architecture assessed in terms of traditional IR metrics at small cutoffs. Experimental results show the effectiveness of our techniques that achieve an improvement of up to $0.28$ (+93%) for P@1 and $0.19$ (+89.9%) for nDCG@3 w.r.t. the CAsT baseline.Source: SIGIR 2020 - 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2057–2060, Online Conference, July 25-30, 2020
DOI: 10.1145/3397271.3401268
DOI: 10.48550/arxiv.2004.14054
Project(s): BigDataGrapes
Metrics:
Mele I., Muntean C. I., Nardini F. M., Perego R., Tonellotto N., Frieder O.
In a conversational context, a user expresses her multi-faceted information need as a sequence of natural-language questions, i.e., utterances. Starting from a given topic, the conversation evolves through user utterances and system replies. The retrieval of documents relevant to a given utterance in a conversation is challenging due to ambiguity of natural language and to the difficulty of detecting possible topic shifts and semantic relationships among utterances. We adopt the 2019 TREC Conversational Assistant Track (CAsT) framework to experiment with a modular architecture performing: (i) topic-aware utterance rewriting, (ii) retrieval of candidate passages for the rewritten utterances, and (iii) neural-based re-ranking of candidate passages. We present a comprehensive experimental evaluation of the architecture assessed in terms of traditional IR metrics at small cutoffs. Experimental results show the effectiveness of our techniques that achieve an improvement of up to $0.28$ (+93%) for P@1 and $0.19$ (+89.9%) for nDCG@3 w.r.t. the CAsT baseline.Source: SIGIR 2020 - 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2057–2060, Online Conference, July 25-30, 2020
DOI: 10.1145/3397271.3401268
DOI: 10.48550/arxiv.2004.14054
Project(s): BigDataGrapes
Metrics:
See at:
arXiv.org e-Print Archive | arxiv.org | dl.acm.org | doi.org | doi.org | CNR ExploRA
2020
Journal article
Restricted
Weighting passages enhances accuracy
Muntean C. I., Nardini F. M., Perego R., Tonellotto N., Frieder O.
We observe that in curated documents the distribution of the occurrences of salient terms, e.g., terms with a high Inverse Document Frequency, is not uniform, and such terms are primarily concentrated towards the beginning and the end of the document. Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 Passage (BM25P), which scores query results by computing a linear combination of term statistics in the different portions of the document. We study a multiplicity of partitioning schemes of document content into passages and compute the collection-dependent weights associated with them on the basis of the distribution of occurrences of salient terms in documents. Moreover, we tune BM25P hyperparameters and investigate their impact on ad hoc document retrieval through fully reproducible experiments conducted using four publicly available datasets. Our findings demonstrate that our BM25P weighting model markedly and consistently outperforms BM25 in terms of effectiveness by up to 17.44% in NDCG@5 and 85% in NDCG@1, and up to 21% in MRR.Source: ACM transactions on information systems 39 (2020). doi:10.1145/3428687
DOI: 10.1145/3428687
Metrics:
Muntean C. I., Nardini F. M., Perego R., Tonellotto N., Frieder O.
We observe that in curated documents the distribution of the occurrences of salient terms, e.g., terms with a high Inverse Document Frequency, is not uniform, and such terms are primarily concentrated towards the beginning and the end of the document. Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 Passage (BM25P), which scores query results by computing a linear combination of term statistics in the different portions of the document. We study a multiplicity of partitioning schemes of document content into passages and compute the collection-dependent weights associated with them on the basis of the distribution of occurrences of salient terms in documents. Moreover, we tune BM25P hyperparameters and investigate their impact on ad hoc document retrieval through fully reproducible experiments conducted using four publicly available datasets. Our findings demonstrate that our BM25P weighting model markedly and consistently outperforms BM25 in terms of effectiveness by up to 17.44% in NDCG@5 and 85% in NDCG@1, and up to 21% in MRR.Source: ACM transactions on information systems 39 (2020). doi:10.1145/3428687
DOI: 10.1145/3428687
Metrics:
See at:
ACM Transactions on Information Systems | CNR ExploRA
2020
Journal article
Open Access
RankEval: Evaluation and investigation of ranking models
Lucchese C., Muntean C. I., Nardini F. M., Perego R., Trani S.
RankEval is a Python open-source tool for the analysis and evaluation of ranking models based on ensembles of decision trees. Learning-to-Rank (LtR) approaches that generate tree-ensembles are considered the most effective solution for difficult ranking tasks and several impactful LtR libraries have been developed aimed at improving ranking quality and training efficiency. However, these libraries are not very helpful in terms of hyper-parameters tuning and in-depth analysis of the learned models, and even the implementation of most popular Information Retrieval (IR) metrics differ among them, thus making difficult to compare different models. RankEval overcomes these limitations by providing a unified environment where to perform an easy, comprehensive inspection and assessment of ranking models trained using different machine learning libraries. The tool focuses on ensuring efficiency, flexibility and extensibility and is fully interoperable with most popular LtR libraries.Source: Softwarex (Amsterdam) 12 (2020). doi:10.1016/j.softx.2020.100614
DOI: 10.1016/j.softx.2020.100614
Project(s): BigDataGrapes
Metrics:
Lucchese C., Muntean C. I., Nardini F. M., Perego R., Trani S.
RankEval is a Python open-source tool for the analysis and evaluation of ranking models based on ensembles of decision trees. Learning-to-Rank (LtR) approaches that generate tree-ensembles are considered the most effective solution for difficult ranking tasks and several impactful LtR libraries have been developed aimed at improving ranking quality and training efficiency. However, these libraries are not very helpful in terms of hyper-parameters tuning and in-depth analysis of the learned models, and even the implementation of most popular Information Retrieval (IR) metrics differ among them, thus making difficult to compare different models. RankEval overcomes these limitations by providing a unified environment where to perform an easy, comprehensive inspection and assessment of ranking models trained using different machine learning libraries. The tool focuses on ensuring efficiency, flexibility and extensibility and is fully interoperable with most popular LtR libraries.Source: Softwarex (Amsterdam) 12 (2020). doi:10.1016/j.softx.2020.100614
DOI: 10.1016/j.softx.2020.100614
Project(s): BigDataGrapes
Metrics:
See at:
SoftwareX | ISTI Repository | SoftwareX | www.sciencedirect.com | CNR ExploRA