Name
Title | ||
|---|---|---|
Self-Attentive Graph Convolution Network With Latent Group Mining and Collaborative Filtering for Personalized Recommendation |
Abstract | ||
|---|---|---|
The remarkable progress of machine learning has led to some state-of-the-art algorithms in personalized recommendation. Previous recommendation algorithms generally learn users’ and items’ representations based on a user-item rating matrix. However, these methods only consider a user's own preference, but ignore the influence of the user's social circles. In this paper, we propose a novel recommendation algorithm,
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Self-Attentive Graph Convolution Network with Latent Group Mining and Collaborative Filtering</i>
, which consists of
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Latent Group Mining</i>
(LGM) module,
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Collaborative Embedding</i>
(CE) module and
<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Self-Attentive Graph Convolution</i>
(SAGC) module. The LGM module analyzes users’ social circles by exploring their latent groups and generates group embedding for users and items. The CE module uses a graph embedding method to provide semantic collaborative embedding for users and items. The SAGC module fuses users’ (items’) collaborative embedding and group embedding by a self-attentive graph convolution network to learn their fine-grained representations for rating prediction. We conduct experiments on different real-world datasets, which validates that our algorithm outperforms the state-of-the-art algorithms. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TNSE.2021.3110677 | IEEE Transactions on Network Science and Engineering |
Keywords | DocType | Volume |
Recommender system,collaborative filtering,machine learning,random walk,graph embedding | Journal | 9 |
Issue | ISSN | Citations |
5 | 2327-4697 | 0 |
PageRank | References | Authors |
0.34 | 33 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shiyuan Liu | 1 | 17 | 8.28 |
| Bang Wang | 2 | 809 | 57.74 |
| Xianjun Deng | 3 | 127 | 15.27 |
| Laurence T. Yang | 4 | 6870 | 682.61 |