A Unified Personalized Video Recommendation via Dynamic Recurrent Neural Networks

Personalized video recommender systems play an essential role in bridging users and videos. However, most existing video recommendation methods assume that user profiles (interests) are static. In fact, the static assumption is inadequate to reflect users' dynamic interests as time goes by, especially in the online video recommendation scenarios with dramatic changes of video contents and frequent drift of users' interests over different topics. To overcome the above issue, we propose a dynamic recurrent neural network to model users' dynamic interests over time in a unified framework for personalized video recommendation. Furthermore, to build a much more comprehensive recommendation system, the proposed model is designed to exploit video semantic embedding, user interest modeling, and user relevance mining jointly to model users' preferences. By considering these three factors, the RNN model becomes an interest network which can capture users' high level interests effectively. Extensive experimental results on both single-network and cross-network video recommendation scenarios demonstrate the superior performance of the proposed model compared with other state-of-the-art algorithms.