2010
Other
Unknown
CIP PSP-BPN ASSETS project: Advanced Search Service and Enhanced Technological Solutions for the Europeana Digital Library
Lucchese C., Perego R., Silvestri F., Tonellotto N.
ASSETS aims to improve the usability of the Europeana Digital Library platform by designing, implementing and deploying large-scale, scalable services for search and browsing. These services include: efficient storing and indexing, searching based on metadata and on content similarity; advanced ranking algorithms; browsing through semantic cross-links; semi-automatic ingestion of metadata requiring normalization, cleaning, knowledge extraction and mapping to a common structure.
Lucchese C., Perego R., Silvestri F., Tonellotto N.
ASSETS aims to improve the usability of the Europeana Digital Library platform by designing, implementing and deploying large-scale, scalable services for search and browsing. These services include: efficient storing and indexing, searching based on metadata and on content similarity; advanced ranking algorithms; browsing through semantic cross-links; semi-automatic ingestion of metadata requiring normalization, cleaning, knowledge extraction and mapping to a common structure.
See at: CNR ExploRA
2010
Conference article
Unknown
Detecting task-based query sessions using collaborative knowledge
Lucchese C., Orlando S., Perego R., Silvestri F., Tolomei G.
Our research challenge is to provide a mechanism for splitting into user task-based sessions a long-term log of queries submitted to a Web Search Engine (WSE). The hypothesis is that some query sessions entail the concept of user task. We present an approach that relies on a centroid-based and a density-based clustering algorithm, which consider queries inter-arrival times and use a novel distance function that takes care of query lexical content and exploits the collaborative knowledge collected by Wiktionary and Wikipedia.Source: 2010 International Workshop on Intelligent Web Interaction, Toronto, Canada, 31 Agosto 2010
Lucchese C., Orlando S., Perego R., Silvestri F., Tolomei G.
Our research challenge is to provide a mechanism for splitting into user task-based sessions a long-term log of queries submitted to a Web Search Engine (WSE). The hypothesis is that some query sessions entail the concept of user task. We present an approach that relies on a centroid-based and a density-based clustering algorithm, which consider queries inter-arrival times and use a novel distance function that takes care of query lexical content and exploits the collaborative knowledge collected by Wiktionary and Wikipedia.Source: 2010 International Workshop on Intelligent Web Interaction, Toronto, Canada, 31 Agosto 2010
See at: CNR ExploRA
2010
Contribution to book
Restricted
Preserving privacy in Web recommender systems
Perego R., Baraglia R., Lucchese C., Orlando S., Silvestri F.
The rapid growth of the Web has led to the development of new solu- tions in the Web recommender or personalization domain, aimed to assist users in satisfying their information needs. The main goal of this chapter is to survey some of the recommender system proposals appeared in the literature, and to evaluate these pro- posals from the point of view of privacy preservation. Then, as an ex- ample of privacy-preserving approach for recommendations, we present ?SUGGEST, a privacy-enhanced system that allows for creating serendip- ity recommendations without breaching users privacy. ?SUGGEST helps users to navigate though a Web site, by providing dynamically generated links to relevant pages that have not yet been visited. The knowledge base on which the model used for making recommendations is built, is incrementally updated without tracking user sessions. This feature is par- ticularly important when users do not trust the system, and do not want disclose their complete activity records or preferences. In this case, users may adopt techniques that avoid server-based session reconstruction, and that do not worsen the accuracy of the model extracted by ?SUGGEST. As an additional contribution, we show that ?SUGGEST does not allow malicious users to track or detect users activity or preferences.Source: Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques, edited by Francesco Bonchi, Yahoo! Research, Barcelona, Spain; Elena Ferrari, University of Insubria, Italy, pp. 369–389. London: CRC Press - Taylor & Francis Group, 2010
Perego R., Baraglia R., Lucchese C., Orlando S., Silvestri F.
The rapid growth of the Web has led to the development of new solu- tions in the Web recommender or personalization domain, aimed to assist users in satisfying their information needs. The main goal of this chapter is to survey some of the recommender system proposals appeared in the literature, and to evaluate these pro- posals from the point of view of privacy preservation. Then, as an ex- ample of privacy-preserving approach for recommendations, we present ?SUGGEST, a privacy-enhanced system that allows for creating serendip- ity recommendations without breaching users privacy. ?SUGGEST helps users to navigate though a Web site, by providing dynamically generated links to relevant pages that have not yet been visited. The knowledge base on which the model used for making recommendations is built, is incrementally updated without tracking user sessions. This feature is par- ticularly important when users do not trust the system, and do not want disclose their complete activity records or preferences. In this case, users may adopt techniques that avoid server-based session reconstruction, and that do not worsen the accuracy of the model extracted by ?SUGGEST. As an additional contribution, we show that ?SUGGEST does not allow malicious users to track or detect users activity or preferences.Source: Privacy-Aware Knowledge Discovery: Novel Applications and New Techniques, edited by Francesco Bonchi, Yahoo! Research, Barcelona, Spain; Elena Ferrari, University of Insubria, Italy, pp. 369–389. London: CRC Press - Taylor & Francis Group, 2010
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