GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback (2111.02036v1)

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.

• The paper introduces a graph-refined convolutional network that prunes false-positive edges from implicit feedback.
• It employs a prototypical network-based refining layer to dynamically filter noisy user-item interactions.
• Experimental results on Movielens, TikTok, and Kwai datasets show significant improvements in precision, recall, and NDCG.

Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback

The research presented in "Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback" offers a novel approach to address a critical challenge in recommender systems: the contamination of user-item interaction graphs by false-positive edges derived from implicit user feedback. The proposed Graph-Refined Convolutional Network (GRCN) method innovatively refines these graphs, consequently boosting the performance of Graph Convolutional Network (GCN)-based recommendation models.

Traditional GCN-based models for recommendation primarily rely on user-item interaction data to construct graphs where nodes represent users and items. Edges in such graphs ostensibly indicate user interest. However, implicit feedback, such as accidental clicks or incidental views, often introduces noise into these graphs, leading to suboptimal user and item representations and degraded recommendation performance. The GRCN directly tackles this problem by dynamically refining graph structures based on the model's training status, thereby mitigating the adverse effects of false-positive interactions.

Central to the GRCN approach is a graph refining layer that employs a prototypical network to adaptively identify and prune noisy edges in the interaction graph. It achieves this by gauging the affinity between user preference prototypes, derived from multimodal item features, and target item representations. Consequently, edges with high confidence scores of being false-positive interactions are pruned, thus filtering out the noise and preserving only informative signals conducive to user preference modeling.

Extensive experimental validation is conducted across three datasets: Movielens, Tiktok, and Kwai. The results corroborate the efficacy of GRCN, evidencing significant improvements in recommendation accuracy compared to state-of-the-art baselines such as MMGCN, NGCF, and GAT across precision, recall, and NDCG metrics. The performance enhancements are attributed to the refined graph structure that facilitates more effective graph convolutional operations by emphasizing true-positive interactions.

In addition to strong numerical results, the implications of this paper extend to practical and theoretical domains of recommender systems. Practically, the GRCN method can be integrated into existing multimedia platforms to enhance the quality of content recommendations and user engagement by correctly capturing user preferences. Theoretically, the approach opens new avenues in exploring adaptive graph refinement techniques for recommender systems, promoting further inquiry into harmonizing graph-based learning models with real-world data complexities.

Future developments could explore refining edge pruning strategies and extending this methodology to multi-domain recommendation contexts. Further research could also integrate user intent modeling to provide a more comprehensive solution to implicit feedback challenges.

In conclusion, this paper significantly advances the understanding and application of GCNs in multimedia recommendation systems by addressing and refining the erroneous structures of interaction graphs. The implications are profound, fostering deeper explorations into adaptive learning mechanisms in AI-driven recommendation systems.