2011.02.01 - 2012.01.31 (1 years). This work was supported by Samsung Electronics.
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 to watch Star Wars or Titanic. Effective recommendation must recommend unexpected or novel items that could surprise users and affect users' purchase decision. This project is to develop an effective recommendation for digital TV customers. | 