Session-Based Recommendation Model Based on Multiple Neural Networks Hybrid Extraction Feature

The problem of session-based recommendation model aims to predict user actions based on anonymous sessions. Although, previous models achieved promising results, there are still some problems, for example, we are unable to take into account the effects of session sequences of different lengths. Generally speaking, the effect of long sequence is not as good as that of short sequence in the same model. The reason of above is that the characteristics of different length session will vary greatly. Generally, the shorter the session, the tighter the relationship between items, and the longer the session, the more likely there are items that have no relationship with each other. So, we propose a model named session-based recommendation model based on multiple neural networks hybrid extraction feature. This model uses different feature extractor to deal with the features of long sessions and short sessions respectively. In SR-MNN, we use Graph Convolutional Network to extract the features of long session and use Recurrent Neural Network to extract the features of short session. Each session is then represented as the composition of the global preference, the initial interest of that session, and the current interest of that session using an attention network. Experiments on two real datasets show that SR-MNN evidently outperforms the state-of-the-art session-based recommendation methods consistently.