Interest-aware Message-Passing GCN for Recommendation

By Fan Liu, Zhiyong Cheng, Lei Zhu, Zan Gao, Liqiang Nie. The Web Conference, 2021

332 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/a7febdc89a0235b7e944512a7e5358e268c8e1c3

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Liqiang Nie
5. Fuli Feng
6. Yong Li
7. Chen Gao
8. Chao Huang

In Computer Networks and Communications

1. Xiangnan He
2. Tat-seng Chua
3. Xiang Wang
4. Liqiang Nie
5. Fuli Feng
6. Yong Li

In Information Systems

1. Xiangnan He
2. Xiang Wang
3. Tat-seng Chua
4. Liqiang Nie
5. Fuli Feng
6. Yong Li
7. Chen Gao
8. Chao Huang

Citations in the corpus

• LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation (2023, background): “In general, to learn user and item representations, GNN-based recommenders perform embedding propagation on the user-item interaction graph by stacking multiple message passing layers for exploring high-order connectivity (He et al., 2020; Zhang et al., 2019; Liu et al., 2021a).”
• A Survey of Graph Neural Networks for Recommender Systems: Challenges, Methods, and Directions (2021, methodology): “Table 5 shows the commonality and diference among the GNN models in matching [104] proposed irst to ilter promising neighbors and then adopt embedding propagation.”
• Contrastive Learning for Cold-Start Recommendation (2021, background): “With the assumption that users with similar behavior prefer similar items, modern collaborative filtering (CF) methods [16, 23, 24] learn collaborative embeddings of users and items from the historical interactions.”
• Diffusion Recommender Model (2023, background): “Discriminative recommender models [20, 43] usually predict user-item interaction probabilities given the user and item representations.”