Sequence Recommendation with Dual Channel Heterogeneous Graph Neural Network

Abstract

The purpose of recommendation system based on user behavior sequence is to predict user??s next click according to the order of last sequence. The current research is generally based on the conversion of items in the user behavior sequence to understand user preferences. However, other valid information in the behavior sequence is ignored, such as the user profile, which results in the model failing to understand user??s specific preferences. In this paper, a user behavior sequence recommendation with dual channel heterogeneous graph neural network (DC-HetGNN) is proposed. The method uses a heterogeneous graph neural network channel and a heterogeneous graph line channel to learn behavior sequence embedding and capture the specific preferences of users. DC-HetGNN constructs heterogeneous graphs containing various types of nodes based on behavior sequences that capture dependencies between projects, users, and sequences. Then, the heterogeneous graph neural network channel and the heterogeneous graph line channel capture the complex transformation of items and the interaction between the sequences, and learn the embedding of items containing user information. Finally, considering the influence of users’ long-term and short-term preferences, local and global sequence embedding is combined with attention network to obtain the final sequence embedding. A large number of experiments conducted on Diginetica and Tmall, two real e-commerce user behavior sequence datasets, show that compared with recent model FGNN, DC-HetGNN is improved by 2.08% and 0.78% on average in performance criterions mean reciprocal rank (MRR) and Recall, respectively, and by 2.70% and 0.49% in performance criterions MRR@n and Recall@n, respectively, compared with recent model TGSRec.

Keywords

• recommender system; user behavior sequence; heterogeneous graph neural network