FIT-recommend ing TV programs to family members

Abstract

The proliferation of cable and satellite TV channels exposes the viewer to a huge variety of contents requiring the investment of substantial efforts in search of interesting programs. Recommendation systems may alleviate the problem by proposing the preferred programs according to prior user choices. However, in most homes, watching TV is a “family” event requiring the recommendation system to represent the individual preferences of the family members, identifying them for recommending the preferred programs of all the current viewers. This work deals with the representation and adaptation of family member preferences. We introduce FIT, a Family Interactive TV system that aims to filter TV programs according to the different viewer's preferences. We assume that the choice of a viewer may change in the presence of other family members. For creating initial family members preferences’ profiles, FIT uses a predefined stereotype user representation along with the user preferred watching times. Implicit relevance feedback is assessed by monitoring the actual viewers watching choices. FIT checks its prediction, updating the member/s preferences and their correlated vector accordingly. In case of error detection, FIT will verify the mistake with the user. We evaluated FIT predictions by comparing it with two other algorithms (one that request user identification, and another that chooses randomly the program genre). Simulation results indicate that FITs performance resembles the system that asks for user identification (without requiring that), and considerably outperforms the random system.