Given who-watched-which TV transaction data, how can we recommend relevant TV programs for a given user? Given a friendship social network, how can we recommend friends that are likely to make connections to a given user? Recommendation is an important application of data mining, and is widely used in movie recommendation, restaurant recommendation, job recommendation, article recommendation, and friend recommendation. In this project, we work on designing and developing models, algorithms, and systems for recommendation. We focus on the following researches.
- Recommendation in multi-modality, where multi-modal data, including ratings, social networks, texts, images, and videos are available.
- Sequence recommendation where we want to predict the next item in a sequence (e.g., video and news recommendation).
- Active recommendation: we devise method to "control" the dynamics of recommender systems, instead of merely observing them.
- Network based recommendation: we work on recommendation in networks or graphs (e.g., "People You May Know" in LinkedIn, or friend recommendation in Facebook) which is a very important problem. We work on fast and scalable models and algorithms for network based recommendation.