Présenté par Sihem Amer-Yahia (Yahoo Researcher, New York, USA) à 10h30 dans la Salle de Séminaire du LIRMM.

Abstract: The fast increasing content on collaborative tagging sites, such as Delicious, and collaborative rating sites, such as MovieLens, requires the development of scalable and efficient search and recommendation techniques. I will describe two concrete applications to illustrate the challenges behind data management for personalized content discovery on those sites. In the first application, network-aware search, I will argue that obvious adaptations of well-known top-k algorithms require to maintain per (seeker, keyword) indexes, due to the dependence of scores on the seeker’s interest network. I will therefore investigate two space-saving solutions for network-aware search, network clustering and behavior clustering. In the second application, group recommendation, the quality of a recommendation is a function of disagreement among group members. This calls for maintaining pair-wise user disagreement indexes. Therefore, I will explore two space-saving strategies for group recommendation, behavior factoring and partial materialization. I will show that scalable and efficient search and recommendation techniques rely on exploring the balance between storage volume and response time in both applications.

Acknowlegdements: Network-aware search is joint work with Michael Benedikt (Oxford), Laks Lakshmanan (UBC) and Julia Stoyanovich (UPenn). Group recommendation is joint work with Gautam Das (UT Arlington), Senjuti Basu Roy (UT Arlington) and Cong Yu (Google Inc)