Research > Recommender Systems
Recommender Systems Research at Technicolor Palo Alto
Recommender Systems Projects
Recommender Systems: Our research in recommender systems spans several topics, including context-aware recommendations, online learning, and matrix completion. We are particularly interested in challenges that arise from exploiting rich user profiles, user social relationships and content meta-data. We also study approaches for giving recommendations to groups of users both inside and outside the home. Finally, we also investigate methods for providing recommendations in a distributed and privacy-preserving fashion.
People
- Sandilya Bhamidipati |
- Jean Bolot |
- Smriti Bhagat |
- Ashwin Kashyap |
- Stratis Ioannidis |
- José Bento |
- Jinyun Yan |
- Branislav Kveton |
- Udi Weinsberg
Publications
- Surfing the Blogosphere: Optimal Personalized Strategies for Searching the Web, International Conference on Computer Communications (INFOCOM), 2010 [PDF]
- Distributed Rating Prediction in User Generated Content Streams, ACM Conference on Recommender Systems (RecSys), 2011 [PDF]
- Content Search Through Comparisons, International Colloquium on Automata, Languages and Programming (ICALP), 2011 [PDF]
- Identifying Users From Their Rating Patterns, Challenge on Context-aware Movie Recommendation (CAMRa), 2011 [PDF]