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However, many existing GCN-based social recommendation methods often directly apply GCN to capture user-item and user-user interactions, which probably have two main limitations: (a) Due to the power-law property of the degree distribution, the vanilla GCN with static normalized adjacency matrix has limitations in learning node representations, especially for the long-tail nodes; (b) multi-typed social relationships between users that are ubiquitous in the real world are rarely considered. In this article, we propose a novel Bilateral Filtering Heterogeneous Attention Network (BFHAN), which improves long-tail node representations and leverages multi-typed social relationships between user nodes. First, we propose a novel graph convolutional filter for the user-item bipartite network and extend it to the user-user homogeneous network. Further, we theoretically analyze the correlation between the convergence values of different graph convolutional filters and node degrees after stacking multiple layers. Second, we model multi-relational social interactions between users as the multiplex network and further propose a multiplex attention network to capture distinctive inter-layer influences for user representations. Last but not least, the experimental results demonstrate that our proposed method outperforms several state-of-the-art GCN-based methods for social recommendation tasks.<\/jats:p>","DOI":"10.1145\/3469799","type":"journal-article","created":{"date-parts":[[2021,9,28]],"date-time":"2021-09-28T04:37:30Z","timestamp":1632803850000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Bilateral Filtering Graph Convolutional Network for Multi-relational Social Recommendation in the Power-law Networks"],"prefix":"10.1145","volume":"40","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2871-0023","authenticated-orcid":false,"given":"Minghao","family":"Zhao","sequence":"first","affiliation":[{"name":"Fuxi AI Lab, NetEase Games"}]},{"given":"Qilin","family":"Deng","sequence":"additional","affiliation":[{"name":"Fuxi AI Lab, NetEase Games"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"Fuxi AI Lab, NetEase Games"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4556-0581","authenticated-orcid":false,"given":"Runze","family":"Wu","sequence":"additional","affiliation":[{"name":"Fuxi AI Lab, NetEase Games"}]},{"given":"Jianrong","family":"Tao","sequence":"additional","affiliation":[{"name":"Fuxi AI Lab, NetEase Games"}]},{"given":"Changjie","family":"Fan","sequence":"additional","affiliation":[{"name":"Fuxi AI Lab, NetEase Games"}]},{"given":"Liang","family":"Chen","sequence":"additional","affiliation":[{"name":"Sun Yat-Sen University, China"}]},{"given":"Peng","family":"Cui","sequence":"additional","affiliation":[{"name":"Tsinghua University, China"}]}],"member":"320","published-online":{"date-parts":[[2021,9,27]]},"reference":[{"key":"e_1_2_1_1_1","volume-title":"Recommender Systems Handbook","author":"Ricci Francesco","unstructured":"Francesco Ricci , Lior Rokach , and Bracha Shapira . 2011. 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