Interest-aware Message-Passing GCN for Recommendation (2102.10044v2)

Abstract: Graph Convolution Networks (GCNs) manifest great potential in recommendation. This is attributed to their capability on learning good user and item embeddings by exploiting the collaborative signals from the high-order neighbors. Like other GCN models, the GCN based recommendation models also suffer from the notorious over-smoothing problem - when stacking more layers, node embeddings become more similar and eventually indistinguishable, resulted in performance degradation. The recently proposed LightGCN and LR-GCN alleviate this problem to some extent, however, we argue that they overlook an important factor for the over-smoothing problem in recommendation, that is, high-order neighboring users with no common interests of a user can be also involved in the user's embedding learning in the graph convolution operation. As a result, the multi-layer graph convolution will make users with dissimilar interests have similar embeddings. In this paper, we propose a novel Interest-aware Message-Passing GCN (IMP-GCN) recommendation model, which performs high-order graph convolution inside subgraphs. The subgraph consists of users with similar interests and their interacted items. To form the subgraphs, we design an unsupervised subgraph generation module, which can effectively identify users with common interests by exploiting both user feature and graph structure. To this end, our model can avoid propagating negative information from high-order neighbors into embedding learning. Experimental results on three large-scale benchmark datasets show that our model can gain performance improvement by stacking more layers and outperform the state-of-the-art GCN-based recommendation models significantly.

Interest-aware Message-Passing GCN for Recommendation: A Summary

The paper "Interest-aware Message-Passing GCN for Recommendation" proposes a novel approach for enhancing recommendation systems using Graph Convolutional Networks (GCNs). The primary contribution of this paper is the introduction of the Interest-aware Message-Passing GCN (IMP-GCN) model, which aims to mitigate the over-smoothing problem commonly encountered in GCN-based recommendation models. Over-smoothing can degrade model performance by making node embeddings increasingly similar as more layers are stacked. Existing approaches like LightGCN and LR-GCN have attempted to address this issue, but the authors suggest these models overlook the detrimental effects of high-order neighbors with dissimilar interests contributing to embedding propagation.

The IMP-GCN model advances prior work by introducing a high-order graph convolution within subgraphs composed of users with similar interests and their interacted items. The key innovation is the design of an unsupervised subgraph generation module that effectively distinguishes users with shared interests by utilizing both user features and graph structure. This approach intends to filter out negative information from high-order neighbors that may otherwise adversely affect embedding learning.

Experimental Results

The paper reports experimental validations conducted on three large-scale benchmark datasets, demonstrating that the IMP-GCN model surpasses state-of-the-art GCN-based recommendation models significantly. The model exhibits improved performance with increased layers, an area where many existing models falter due to over-smoothing. The experiments reveal that by considering interest-aware neighbor contributions, the IMP-GCN model can better leverage high-order connectivity to enhance recommendation accuracy.

Methodological Overview

1. Subgraph Construction: IMP-GCN forms subgraphs by grouping users with shared interests. These subgraphs serve as the domain within which high-order graph convolutions are conducted, preventing the degradation of embedding specificity.
2. Graph Convolution: The model employs a simplified network structure akin to LightGCN, maintaining efficiency while incorporating message-passing mechanisms that confine information propagation within subgraphs, thus retaining the uniqueness of user embeddings.
3. Performance Metrics: The effectiveness of the IMP-GCN model was evaluated using standard recommendation metrics, Recall and Normalized Discounted Cumulative Gain (NDCG), achieving substantial improvements over baselines at varying depth levels of graph convolution.

Implications and Future Work

From a theoretical perspective, the work underscores the significance of interest-aware filtering in alleviating the over-smoothing problem. Practically, deploying models such as IMP-GCN can lead to more personalized and accurate recommendation systems by integrating subtle nuances in user interest and decision patterns.

Moving forward, the authors call attention to the potential for further exploring and adapting subgraph-based methods in other domains of graph learning and recommendation. They propose expanding the model's scope to incorporate additional user-item interaction contexts and scalability to handle larger datasets. Moreover, integrating implicit feedback mechanisms into IMP-GCN could provide further insights into user behaviors, enhancing the model's robustness and real-world applicability.

In conclusion, the paper presents a substantive enhancement in GCN-based recommendation models by effectively addressing the over-smoothing issue through subgraph-based interest-aware approaches. Such advancements hold promise for optimizing recommendation systems in diverse application scenarios, contributing meaningfully to the field of personalized information retrieval.