An Improved Dynamic Collaborative Filtering Algorithm Based on LDA

Currently, available collaborative filtering (CF) algorithms often utilize user behavior data to generate recommendations. The similarity calculation between users is mostly based on the scores, without considering the explicit attributes of the users with profiles, as these are difficult to generate, or their preferences over time evolve. This paper proposes a collaborative filtering algorithm named hybrid dynamic collaborative filtering (HDCF), which is based on the topic model. Considering that the user's evaluation of an item will change over time, we add a time-decay function to the subject model and give its variational inference model. In the collaborative filtering score, we generate a hybrid score for similarity calculation with the topic model. The experimental results show that this algorithm has better performance than currently available algorithms on the MovieLens dataset, Netflix dataset and la.fm dataset.