A Multi-Head Attention Based Dual Target Graph Collaborative Filtering Network

Abstract

Recently, cross-domain collaborative filtering (CDCF) has been widely used to solve the data sparsity problem in recommendation systems. Therein, the dual-target cross-domain recommendation becomes a research hotspot, which aims to improve the recommendation performance of both target and source domains. Most existing approaches tend to use fixed weights or self-attention in a single representation space for the bi-directional inter-domain transfer of the user representation. However, a single representation space leads to limited representation capability, which makes the transfer of the user representation coarse-grained and inaccurate. In this paper, Multi-head Attention Based Dual Target Graph Collaborative Filtering Network (MA-DTGCF) is proposed. The core of the model is the bi-directional transfer graph convolution layer, consisting of a graph convolution layer and a bi-directional transfer layer based on a multi-head attention mechanism. The latter can achieve fine-grained and adaptive transfer of user features in multiple representation subspaces. It is worth noting that by stacking multiple bi-directional transfer graph convolutional layers, we can get high-order user and item features and achieve adaptive transfer of each order user features. Experimental results on three real datasets show that the proposed MA-DTGCF model significantly outperforms the state-of-the-art models in terms of HR and NDCG.