{"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/session-based-recommendation-with-graph","title":"Session-based Recommendation with Graph Neural Networks","arxiv_id":"1811.00855","date":"2018-11-01","proceeding":null,"authors":["Shu Wu","Yuyuan Tang","Yanqiao Zhu","Liang Wang","Xing Xie","Tieniu Tan"],"abstract":"The problem of session-based recommendation aims to predict user actions\nbased on anonymous sessions. Previous methods model a session as a sequence and\nestimate user representations besides item representations to make\nrecommendations. Though achieved promising results, they are insufficient to\nobtain accurate user vectors in sessions and neglect complex transitions of\nitems. To obtain accurate item embedding and take complex transitions of items\ninto account, we propose a novel method, i.e. Session-based Recommendation with\nGraph Neural Networks, SR-GNN for brevity. In the proposed method, session\nsequences are modeled as graph-structured data. Based on the session graph, GNN\ncan capture complex transitions of items, which are difficult to be revealed by\nprevious conventional sequential methods. Each session is then represented as\nthe composition of the global preference and the current interest of that\nsession using an attention network. Extensive experiments conducted on two real\ndatasets show that SR-GNN evidently outperforms the state-of-the-art\nsession-based recommendation methods consistently.","url_abs":"http://arxiv.org/abs/1811.00855v4","url_pdf":"http://arxiv.org/pdf/1811.00855v4.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-recommendation-with-graph","repo_url":"https://github.com/CRIPAC-DIG/SR-GNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"session-based-recommendation-with-graph","repo_url":"https://github.com/DiMarzioRock7/SR-GNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"session-based-recommendation-with-graph","repo_url":"https://github.com/herrbilbo/hse-recsys-project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"session-based-recommendation-with-graph","repo_url":"https://github.com/hkust-knowcomp/sessioncqa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"session-based-recommendation-with-graph","repo_url":"https://github.com/rithinch/session-based-vehicle-recommendations","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"session-based-recommendation-with-graph","repo_url":"https://github.com/userbehavioranalysis/SR-GNN_PyTorch-Geometric","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"session-based-recommendation-with-graph","repo_url":"https://github.com/xiaominglalala/Session_based_Recommendation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"paddle","reach":{"status":"ok"}},{"paper_slug":"session-based-recommendation-with-graph","repo_url":"https://github.com/PaddlePaddle/PaddleRec/tree/release/1.8.5/models/recall/gnn/","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"paddle","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"session-based-recommendations","task_name":"Session-Based Recommendations"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/session-based-recommendations-on-diginetica","task":"Session-Based Recommendations","dataset":"Diginetica","model":"SR-GNN","rank_in_archive_order":9,"of":13,"metrics":{"Hit@20":"50.73","MRR@20":"17.59"},"uses_additional_data":false},{"leaderboard":"/sota/session-based-recommendations-on-gowalla","task":"Session-Based Recommendations","dataset":"Gowalla","model":"SR-GNN","rank_in_archive_order":1,"of":1,"metrics":{"HR@20":"50.32","MRR@20":"24.25"},"uses_additional_data":false},{"leaderboard":"/sota/session-based-recommendations-on-last-fm","task":"Session-Based Recommendations","dataset":"Last.FM","model":"SR-GNN","rank_in_archive_order":3,"of":3,"metrics":{"HR@20":"22.33","MRR@20":"8.23"},"uses_additional_data":false},{"leaderboard":"/sota/session-based-recommendations-on-yoochoose1","task":"Session-Based Recommendations","dataset":"yoochoose1","model":"SR-GNN","rank_in_archive_order":4,"of":4,"metrics":{"MRR@20":"30.94","Precision@20":"70.57"},"uses_additional_data":false},{"leaderboard":"/sota/session-based-recommendations-on-yoochoose1-4","task":"Session-Based Recommendations","dataset":"yoochoose1/4","model":"SR-GNN","rank_in_archive_order":4,"of":4,"metrics":{"HR@20":"71.36","MRR@20":"31.89"},"uses_additional_data":false},{"leaderboard":"/sota/session-based-recommendations-on-yoochoose1-1","task":"Session-Based Recommendations","dataset":"yoochoose1/64","model":"SR-GNN","rank_in_archive_order":9,"of":11,"metrics":{"HR@20":"70.57","MRR@20":"30.94"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.00855","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.00855"}},"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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