Name
Abstract | ||
|---|---|---|
To relieve information flood on the web, recommender system has been widely used to retrieve personalized information. In recommender system, Graph Convolutional Network (GCN) has become a new frontier technology of collaborative filtering. However, existing methods usually assume that neighbor nodes have only positive effects on the target node. A few methods analyze the design of traditional GCNs and eliminate some invalid operations. However, they have not considered the possible negative effects of neighbors to adapt collaborative filtering. Thus, we argue that it is crucial to take the positive and negative effects of neighbors into consideration for collaborative filtering. In this paper, we aim to alter the neighbor aggregation method and layer combination mechanism of GCN to make it more applicable for recommendation. Inspired by LightGCN, we propose a new model named LGACN (Light Graph Adaptive Convolution Network), including the most important component in GCN - neighborhood aggregation and layer combination - for collaborative filtering and alter them to fit recommendations. Specifically, LGACN learns user and item embeddings by propagating their positive and negative information on the user-item interaction graph by an adaptive attention-based method and uses the self-attention mechanism to combine the embeddings learned at each layer as the final embedding. Such a neat model is not only easy to implement but also interpretable, outperforming strong recommender baselines. Our model achieves about 15% relative improvement on Amazonbook and 5% relative improvement on Yelp2018 compared with Light-GCN. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/978-3-030-86365-4_10 | ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2021, PT III |
Keywords | DocType | Volume |
Collaborative filtering, Recommendation, Graph Convolution Network | Conference | 12893 |
ISSN | Citations | PageRank |
0302-9743 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiguang Jiang | 1 | 0 | 0.34 |
| Su Wang | 2 | 1 | 2.09 |
| Jun Zheng | 3 | 18 | 4.87 |
| Wenxin Hu | 4 | 0 | 0.34 |