Siamese Graph-Based Dynamic Matching for Collaborative Filtering

Abstract

Behaviorally similar neighbors in the interaction graph have been actively explored to facilitate the collaboration between users and items and address the interaction sparsity issue. We investigate homogenous neighbors between users or items to mine collaborative signals for embedding learning. In the case of multiple and complex composition of user interests and item attributes, traditional uniform embedding is insufficient to depict matching between a specific user-item pair. Therefore, we propose a siamese graph-based dynamic matching (SGDM) model for collaborative filtering. A target-aware dual attention module is introduced to update neighbors with their varying contributions to unveil users’ interests, which explicitly encodes the clue of the candidate matching target. For a target user-item pair, homogeneous neighbors participate in the interest propagation through graph convolution, which learns user/item embeddings respectively on the siamese homogeneous graphs. The dual dynamic aggregation in graph convolution endows a specific user-item pair with dynamic matching, which is expected to meet the needs of fine-grained filtering and promote the performance of collaborative filtering. Extensive experimental results confirm the collaboration between siamese homogeneous graphs of users and items. It further illustrates the effectiveness of the proposed SGDM in mining homogeneous collaborative signals for embedding learning and collaborative filtering.