Learning Sequential General Pattern and Dependency via Hybrid Neural Model for Session-Based Recommendation

Abstract

Recent study shows that recommendation system not only relys on user’s static preference, but also dynamic preference. Consequently, it leads to the emergence of session-based recommendation. With the development of recurrent neural network, this kind of method can capture representations of users’ sequential behaviors from a large number of sessions. However it is prone to spurious dependency problem. Recently, convolutional neural network has also shown its potential in modelling session, especially in extracting complex local pattern of subsequence. Therefore, we propose a hybrid neural model, called SGPD, for learning sequential general pattern and dependency for session-based recommendation. In SGPD, we propose recurrent residual convolution network to extract general pattern of subsequence in a session. Furthermore, the SGPD scans sequence from forward and reverse direction by bidirectional recurrent neural network, and learns sequential dependency of a session. Finally, the objective function is constructed by cross entropy and the model parameters are learned. The experimental results show that the precision rate, recall rate and mean reciprocal ranking of SGPD are greatly improved compared with the state-of-art methods. It has good application prospect.