Personalized TV Recommendation: Fusing User Behavior and Preferences
This work addresses personalized TV recommendation for viewers, but it appears incremental as it combines existing techniques like user behavior and preferences in a two-stage ranking.
The paper tackles the problem of recommending linear TV programs by proposing a two-stage ranking approach that fuses user behavior and preferences, achieving superior performance in accuracy and time efficiency on a real-world dataset.
In this paper, we propose a two-stage ranking approach for recommending linear TV programs. The proposed approach first leverages user viewing patterns regarding time and TV channels to identify potential candidates for recommendation and then further leverages user preferences to rank these candidates given textual information about programs. To evaluate the method, we conduct empirical studies on a real-world TV dataset, the results of which demonstrate the superior performance of our model in terms of both recommendation accuracy and time efficiency.