Abstract
As an important part of the recommendation system, movie recommendation system can recommend movies to users accurately according to their preferences. Traditional movie recommendation systems simply treat user-movie interactions as a time-ordered sequence, without considering the time intervals between movies of the same genre. The genre time interval can reflect the user’s preference for a particular genre and determine whether the algorithm can fully capture the user’s interests and the time characteristics of the movie, which plays an important role in the accuracy of the movie recommendation. Therefore, in this paper, we propose a Self-Attention Sequential Recommendation algorithm based on Movie Genre Time Interval (SSR-MGTI). Specifically, a multi-head self-attention mechanism is used to model the same genre time interval information. Then, an absolute position is added to the multi-head self-attention mechanism model to solve the problem that multi-head self-attention mechanism does not consider the sequence. In addition, the convolutional neural network is used to convert the model from linear to non-linear and extract local information of user-movie interaction sequences. It is interesting to show that the proposed SSR-MGTI can accurately predict the movie that the user will watch next time. Experimental results on MovieLens and Amazon datasets demonstrate the superiority of our SSR-MGTI over state-of-the-art movie recommendation methods.