Existing recommendation systems (e.g., the Netflix competition) focus on an accurate prediction of purchase, as the systems are evaluated based on the prediction accuracy. However, such systems tend to recommend popular items. Recommending popular items, however, might not be effective or affective on users' purchase decisions, as users likely already know the items and likely have pre-made decisions on the purchase of items, e.g., recommend watching Star Wars or Titanic. An effective recommendation must recommend unexpected or novel items that could surprise users and affect users' purchase decisions. This project is to develop an effective recommendation for digital TV customers.

Feature weighting for ranking has not been researched as extensively as for classification. This project develops various feature weighting methods for ranking by leveraging existing methods for classification. The developed methods are used on the Live Search query log data to identify key features that determine the users’ click-through behaviors. The developed methods will also be used to build the feature selection component of RefMed -- relevance feedback PubMed search engine.

Most online data retrieval systems, built based on relational database management systems(RDBMS), support fast processing of Boolean queries but offer little support for relevance or preference ranking. A unified support of Boolean and ranking constraints in a query is essential for user-friendly data retrieval. This project develops foundational techniques that enable such data retrieval systems in which users intuitively express ranking constraints and the system efficiently processes the queries.

We conducted research with Prof. Jaewook Lee (PI).

Our team has a proven track record of delivering high-impact data science and AI solutions for enterprises and organizations. Here are some notable examples of our work: