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In that context, recommending relevant information to users\nbecomes critical for viability. However, recommendation in online communities\nis a challenging problem: 1) users' interests are dynamic, and 2) users are\ninfluenced by their friends. Moreover, the influencers may be\ncontext-dependent. That is, different friends may be relied upon for different\ntopics. Modeling both signals is therefore essential for recommendations.\n  We propose a recommender system for online communities based on a\ndynamic-graph-attention neural network. We model dynamic user behaviors with a\nrecurrent neural network, and context-dependent social influence with a\ngraph-attention neural network, which dynamically infers the influencers based\non users' current interests. The whole model can be efficiently fit on\nlarge-scale data. Experimental results on several real-world data sets\ndemonstrate the effectiveness of our proposed approach over several competitive\nbaselines including state-of-the-art models.","url_abs":"http://arxiv.org/abs/1902.09362v2","url_pdf":"http://arxiv.org/pdf/1902.09362v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"session-based-social-recommendation-via","repo_url":"https://github.com/DeepGraphLearning/RecommenderSystems","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"session-based-social-recommendation-via","repo_url":"https://github.com/sparsh-ai/RecommenderSystems","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"gone","observed_at":"2026-09-18","how":"tree_404+repo_404"}}],"tasks":[{"task_slug":"graph-attention","task_name":"Graph Attention"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/recommendation-systems-on-delicious","task":"Recommendation Systems","dataset":"Delicious","model":"DGRec","rank_in_archive_order":1,"of":1,"metrics":{"NDCG":"0.2944","Recall@20":"0.4066"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-douban","task":"Recommendation Systems","dataset":"Douban","model":"DGRec","rank_in_archive_order":6,"of":7,"metrics":{"NDCG":"0.195","Recall@20":"0.1861"},"uses_additional_data":false},{"leaderboard":"/sota/recommendation-systems-on-yelp","task":"Recommendation Systems","dataset":"Yelp","model":"DGRec","rank_in_archive_order":1,"of":2,"metrics":{"NDCG":"0.1427","Recall@20":"0.0842"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1902.09362","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1902.09362"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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