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The steady growth in the number of online users has led to the emergence of various online services such as Social Networks (Google+, Facebook, Twitter), e-commerce services (Movies: IMDB, Music: last.fm, Books: Goodreads). These online services leverage personalization schemes mostly Collaborative Filtering. Collaborative filtering schemes leverage profiles of other users to improve personalization quality. On the other hand, it opens up scalability and privacy issues. Additionally, recommenders also suffer from lack of explicit feedback (cold-start) from users.
Scalability for recommenders stems from the fact that these services need to provide personalized recommendations to millions of customers in real-time. A project here for instance would consist in experimenting scalable solutions to recommend appropriate items to web users based on some collaborative filtering protocol.
 HyRec: Leveraging Browsers for Scalable Recommenders
 StreamRec: A Real-Time Recommender System
Recent research shows that customers stop using the services if they face privacy concerns. Hence, designing privacy preserving recommender is one of the major challenges at present. A project here for instance would consist in designing mechanisms which protect privacy of users in online recommender systems.
 D2P: Distance-Based Differential Privacy in Recommenders
 Differential privacy for neighborhood-based Collaborative Filtering
Due to sparsity of data in one domain, we need to explore multiple domains to improve the user experience. A project here for instance would consist in designing an efficient mechanism to extract user profiles from one domain (e.g. Movies). These profiles should be general enough to improve recommendation quality in a different domain (e.g. Books).
 Can Movies and Books Collaborate? Cross-Domain Collaborative Filtering for Sparsity Reduction
 Cross-domain Recommendations without Overlapping Data: Myth or Reality?
Revenue Maximization in Recommender
The major concern for e-commerce sites is Revenue. A project here would consist in designing an efficient revenue-maximizing algorithm and demonstrate its superiority when compared to standard ones without any revenue optimizations.
 Show me the money: dynamic recommendations for revenue maximization
 RecMax: Exploiting Recommender Systems for Fun and Profit
Users normally do not prefer giving explicit feedbacks like ratings. Even the ratings provided by users can vary based on their moods at any given point of time. Hence, recommenders can rely on implicit behaviour of users like clicks or consumption order. A project here for instance would consist in designing an implicit feedback (like timestamp of item consumption) based mechanism for providing recommendations to users without impacting quality significantly when compared to standard ones leveraging explicit feedback.
 A time-based approach to effective recommender systems using implicit feedback
Preference change in Recommender
Predicting preference change in recommender is an interesting research direction which can lead to better recommendation quality. For e.g. if a recommender can predict that the future user preference will vary at a given point of time with high probability then it can adapt its recommendations which standard recommenders can't. A project here for instance would consist of designing an algorithm that can predict such preference changes of users with high probability and demonstrate its superiority when compared to standard ones.
 SemanticSVD++: Incorporating Semantic Taste Evolution for Predicting Ratings
Further details of on-going works can be found at: Google Web-Alter-Egos
Contact: Rhicheek Patra