GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

TL;DR

This paper introduces GRCN, a graph neural network that adaptively refines user-item interaction graphs by pruning false-positive edges, improving recommendation accuracy in implicit feedback scenarios.

Contribution

The paper proposes a novel GCN model with a graph refining layer that dynamically prunes noisy edges, enhancing recommendation performance over fixed-structure models.

Findings

GRCN outperforms baseline models on three micro-video datasets.

Refined graphs lead to more accurate user preference representations.

The adaptive pruning mechanism effectively reduces noise in implicit feedback data.

Abstract

Reorganizing implicit feedback of users as a user-item interaction graph facilitates the applications of graph convolutional networks (GCNs) in recommendation tasks. In the interaction graph, edges between user and item nodes function as the main element of GCNs to perform information propagation and generate informative representations. Nevertheless, an underlying challenge lies in the quality of interaction graph, since observed interactions with less-interested items occur in implicit feedback (say, a user views micro-videos accidentally). This means that the neighborhoods involved with such false-positive edges will be influenced negatively and the signal on user preference can be severely contaminated. However, existing GCN-based recommender models leave such challenge under-explored, resulting in suboptimal representations and performance. In this work, we focus on adaptively refining the structure of interaction graph to discover and prune potential false-positive edges. Towards this end, we devise a new GCN-based recommender model, \emph{Graph-Refined Convolutional Network} (GRCN), which adjusts the structure of interaction graph adaptively based on status of model training, instead of remaining the fixed structure. In particular, a graph refining layer is designed to identify the noisy edges with the high confidence of being false-positive interactions, and consequently prune them in a soft manner. We then apply a graph convolutional layer on the refined graph to distill informative signals on user preference. Through extensive experiments on three datasets for micro-video recommendation, we validate the rationality and effectiveness of our GRCN. Further in-depth analysis presents how the refined graph benefits the GCN-based recommender model.