{"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/recurrent-neural-networks-with-top-k-gains","title":"Recurrent Neural Networks with Top-k Gains for Session-based Recommendations","arxiv_id":"1706.03847","date":"2017-06-12","proceeding":"ICLR 2018 1","authors":["Balázs Hidasi","Alexandros Karatzoglou"],"abstract":"RNNs have been shown to be excellent models for sequential data and in\nparticular for data that is generated by users in an session-based manner. The\nuse of RNNs provides impressive performance benefits over classical methods in\nsession-based recommendations. In this work we introduce novel ranking loss\nfunctions tailored to RNNs in the recommendation setting. The improved\nperformance of these losses over alternatives, along with further tricks and\nrefinements described in this work, allow for an overall improvement of up to\n35% in terms of MRR and Recall@20 over previous session-based RNN solutions and\nup to 53% over classical collaborative filtering approaches. Unlike data\naugmentation-based improvements, our method does not increase training times\nsignificantly. We further demonstrate the performance gain of the RNN over\nbaselines in an online A/B test.","url_abs":"http://arxiv.org/abs/1706.03847v3","url_pdf":"http://arxiv.org/pdf/1706.03847v3.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":"recurrent-neural-networks-with-top-k-gains","repo_url":"https://github.com/hidasib/GRU4Rec","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"recurrent-neural-networks-with-top-k-gains","repo_url":"https://github.com/SharonLSY/CDRC-MSc---Recommender-Systems","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"recurrent-neural-networks-with-top-k-gains","repo_url":"https://github.com/SharonLSY/CDRC-MSc-Recommender-Systems","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"recurrent-neural-networks-with-top-k-gains","repo_url":"https://github.com/bekleyis95/RNN-RecSys","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"recurrent-neural-networks-with-top-k-gains","repo_url":"https://github.com/hidasib/gru4rec_pytorch_official","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"recurrent-neural-networks-with-top-k-gains","repo_url":"https://github.com/hidasib/gru4rec_tensorflow_official","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"recurrent-neural-networks-with-top-k-gains","repo_url":"https://github.com/hungpthanh/gru4rec-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"recurrent-neural-networks-with-top-k-gains","repo_url":"https://github.com/hungthanhpham94/GRU4REC-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"recurrent-neural-networks-with-top-k-gains","repo_url":"https://github.com/nwuAI/Circular-Neural-Network-With-Time-Perception","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"recurrent-neural-networks-with-top-k-gains","repo_url":"https://github.com/otto-de/recsys-dataset","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"recurrent-neural-networks-with-top-k-gains","repo_url":"https://github.com/pcerdam/KerasGRU4Rec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"recurrent-neural-networks-with-top-k-gains","repo_url":"https://github.com/yeganegi-reza/torch-gru4rec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"session-based-recommendations","task_name":"Session-Based Recommendations"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1706.03847","atlas_url":"https://app.syntology.ai/?focus=1706.03847","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1706.03847"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+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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