{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/190412575","title":"Knowledge Graph Convolutional Networks for Recommender Systems","arxiv_id":"1904.12575","date":"2019-03-18","proceeding":null,"authors":["Hongwei Wang","Miao Zhao","Xing Xie","Wenjie Li","Minyi Guo"],"abstract":"To alleviate sparsity and cold start problem of collaborative filtering based\nrecommender systems, researchers and engineers usually collect attributes of\nusers and items, and design delicate algorithms to exploit these additional\ninformation. In general, the attributes are not isolated but connected with\neach other, which forms a knowledge graph (KG). In this paper, we propose\nKnowledge Graph Convolutional Networks (KGCN), an end-to-end framework that\ncaptures inter-item relatedness effectively by mining their associated\nattributes on the KG. To automatically discover both high-order structure\ninformation and semantic information of the KG, we sample from the neighbors\nfor each entity in the KG as their receptive field, then combine neighborhood\ninformation with bias when calculating the representation of a given entity.\nThe receptive field can be extended to multiple hops away to model high-order\nproximity information and capture users' potential long-distance interests.\nMoreover, we implement the proposed KGCN in a minibatch fashion, which enables\nour model to operate on large datasets and KGs. We apply the proposed model to\nthree datasets about movie, book, and music recommendation, and experiment\nresults demonstrate that our approach outperforms strong recommender baselines.","url_abs":"http://arxiv.org/abs/1904.12575v1","url_pdf":"http://arxiv.org/pdf/1904.12575v1.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":"190412575","repo_url":"https://github.com/hwwang55/KGCN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"190412575","repo_url":"https://github.com/KanchiShimono/KGCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"190412575","repo_url":"https://github.com/Ki-Seki/KGCN-pytorch-updated","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"190412575","repo_url":"https://github.com/johnnyjana730/MVIN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"190412575","repo_url":"https://github.com/mostsuperman/KGCN-ML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"190412575","repo_url":"https://github.com/mostsuperman/test","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"190412575","repo_url":"https://github.com/youngch12/Cluster_KGCN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"190412575","repo_url":"https://github.com/zzaebok/KGCN-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"click-through-rate-prediction","task_name":"Click-Through Rate Prediction"},{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"music-recommendation","task_name":"Music Recommendation"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[{"method_slug":"graph-convolutional-networks","method_name":"Graph Convolutional Networks"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/click-through-rate-prediction-on-book","task":"Click-Through Rate Prediction","dataset":"Book-Crossing","model":"KGCN-sum","rank_in_archive_order":1,"of":2,"metrics":{"AUC":"0.738","F1":"0.688"},"uses_additional_data":false},{"leaderboard":"/sota/click-through-rate-prediction-on-lastfm","task":"Click-Through Rate Prediction","dataset":"Last.FM","model":"KGCN-concat","rank_in_archive_order":1,"of":2,"metrics":{"AUC":"0.796","F1":"0.721"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-movielens-25m","task":"Link Prediction","dataset":"MovieLens 25M","model":"KGCN","rank_in_archive_order":7,"of":7,"metrics":{"Hits@10":"0.771","nDCG@10":"0.4699"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-yelp","task":"Link Prediction","dataset":"Yelp","model":"KGCN","rank_in_archive_order":5,"of":9,"metrics":{"HR@10":"0.8125","nDCG@10":"0.4668"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.12575","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1904.12575"}},"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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