This thesis explores the effects of incorporating user consumption behavior and multiple types of user feedback to improve recommender systems for personalized music video television. An industrial use case is made possible by the availability of anonymized user interaction data on curation-based personalized music television system provided by XITE, a music video television broadcasting company in Amsterdam. The characteristics of the curation-based system motivates us to explore the effects of user behavior and feedback on two tasks: session reranking and like prediction task. For the session reranking task, an improvement, in terms of Mean Average Precision (MAP), is achieved by leveraging behavior toward playback of repeated item consumption, together with the implicit user preference which is inferred from personalized average playback ratio for each video. Three types of feedback are used for the `like' prediction task: explicit feedback when user presses like on a video, and implicit feedback in the form of skipping and watching a video completely. A multi-level sampler within Bayesian Personalized Ranking algorithm is used to exploit those types of feedback, and an improvement is obtained compared to using only one type of explicit feedback. Finally, considering common behavior that people often turns on the television while not actively paying attention to it, we show that performing heuristic cut-off, by only considering few music videos watched completely after an active action is taken by the user on the system as positive implicit feedback, could improve the MAP compared to assuming positive implicit feedback for all videos watched completely by the user.