GDSRec: Graph-Based Decentralized Collaborative Filtering for Social Recommendation

TL;DR

GDSRec introduces a graph neural network model that incorporates vectorized user and item biases and differentiable social connections to improve social recommendation accuracy.

Contribution

The paper proposes GDSRec, a novel graph-based decentralized collaborative filtering method that models biases as vectors and integrates social connection strength based on preference similarity.

Findings

GDSRec outperforms state-of-the-art baselines on benchmark datasets.

Vectorized bias modeling improves recommendation quality.

Incorporating social connection strength enhances user similarity capture.

Abstract

Generating recommendations based on user-item interactions and user-user social relations is a common use case in web-based systems. These connections can be naturally represented as graph-structured data and thus utilizing graph neural networks (GNNs) for social recommendation has become a promising research direction. However, existing graph-based methods fails to consider the bias offsets of users (items). For example, a low rating from a fastidious user may not imply a negative attitude toward this item because the user tends to assign low ratings in common cases. Such statistics should be considered into the graph modeling procedure. While some past work considers the biases, we argue that these proposed methods only treat them as scalars and can not capture the complete bias information hidden in data. Besides, social connections between users should also be differentiable so that users with similar item preference would have more influence on each other. To this end, we propose Graph-Based Decentralized Collaborative Filtering for Social Recommendation (GDSRec). GDSRec treats the biases as vectors and fuses them into the process of learning user and item representations. The statistical bias offsets are captured by decentralized neighborhood aggregation while the social connection strength is defined according to the preference similarity and then incorporated into the model design. We conduct extensive experiments on two benchmark datasets to verify the effectiveness of the proposed model. Experimental results show that the proposed GDSRec achieves superior performance compared with state-of-the-art related baselines. Our implementations are available in \url{https://github.com/MEICRS/GDSRec}.