Neural Collaborative Learning for User Preference Discovery From Biased Behavior Sequences

Abstract

The rapid increase of the data of user behaviors on the Internet brings a promising chance to better discover user preferences. Recommender systems have become a popular tool for the discovery of user preferences. One key issue is how to employ user behavior sequences to develop effective sequential recommendations, especially when behavior sequences are biased. The current sequential recommendation methods either can only mine data dependencies but ignores bias or only can learn bias but cannot mine data dependencies. To solve these problems, in this article, we propose a neural collaborative sequential learning mechanism, which learns sequential information from user behavior sequences that contain bias. We propose a neural collaborative filtering (NCF) model that fully takes advantage of all data dependencies among users, items, and biased sequential behaviors. Our sequential learning mechanism employs a self-attention mechanism to learn sequential features into an embedding space and inputs this sequential embedding into the generalized matrix factorization (GMF) model and the multilayer perceptron (MLP) model. We performed experiments on two real-world datasets and compared our model with many well-known baselines. The experimental results demonstrate that our model achieves superior performance. We also give a thorough analysis through ablation experiments and sensitivity experiments.