Disentangled Sequential Variational Autoencoder for Collaborative Filtering

Abstract

Recommendation models typically use user’s historical behaviors to obtain user preference representations for recommendations.Most of the methods of learning user representations always entangle different preference factors,while the disentangled learning method can be used to decompose user behavior characteristics.In this paper,a variational autoencoder based framework DSVAECF is proposed to disentangle the static and dynamic factors from user’s historical behaviors.Firstly,two encoders of the model use multi-layer perceptron and recurrent neural network to model the user behavior history respectively,so as to obtain the static and dynamic preference representation of the user.Then,the concatenate static and dynamic preference representations are treated as disentangled representation input decoders to capture user’s decisions and reconstruct user’s behavior.On the one hand,in the model training phase,DSVAECF learns model parameters by maximizes the mutual information between reconstructed user’s behaviors and actual user’s behaviors.On the other hand,DSVAECF minimizes the difference between disentangled representations and their prior distribution to retain the generation ability of the model.Experimental results on Amazon and MovieLens data sets show that,compared with the baselines,DSVAECF significantly improves the normalized discounted cumulative gain,recall,and precision,and has better recommendation performance.