{"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/hierarchical-gating-networks-for-sequential","title":"Hierarchical Gating Networks for Sequential Recommendation","arxiv_id":"1906.09217","date":"2019-06-21","proceeding":null,"authors":["Chen Ma","Peng Kang","Xue Liu"],"abstract":"The chronological order of user-item interactions is a key feature in many recommender systems, where the items that users will interact may largely depend on those items that users just accessed recently. However, with the tremendous increase of users and items, sequential recommender systems still face several challenging problems: (1) the hardness of modeling the long-term user interests from sparse implicit feedback; (2) the difficulty of capturing the short-term user interests given several items the user just accessed. To cope with these challenges, we propose a hierarchical gating network (HGN), integrated with the Bayesian Personalized Ranking (BPR) to capture both the long-term and short-term user interests. Our HGN consists of a feature gating module, an instance gating module, and an item-item product module. In particular, our feature gating and instance gating modules select what item features can be passed to the downstream layers from the feature and instance levels, respectively. Our item-item product module explicitly captures the item relations between the items that users accessed in the past and those items users will access in the future. We extensively evaluate our model with several state-of-the-art methods and different validation metrics on five real-world datasets. The experimental results demonstrate the effectiveness of our model on Top-N sequential recommendation.","url_abs":"https://arxiv.org/abs/1906.09217v1","url_pdf":"https://arxiv.org/pdf/1906.09217v1.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":"hierarchical-gating-networks-for-sequential","repo_url":"https://github.com/allenjack/HGN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"hierarchical-gating-networks-for-sequential","repo_url":"https://github.com/wuliwei9278/HGN_baseline","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"sequential-recommendation","task_name":"Sequential Recommendation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/recommendation-systems-on-amazon-cds","task":"Recommendation Systems","dataset":"Amazon-CDs","model":"HGN","rank_in_archive_order":2,"of":2,"metrics":{"Recall@10":"0.0426","nDCG@10":"0.0233"},"uses_additional_data":false},{"leaderboard":"/sota/recommendation-systems-on-goodreads-children","task":"Recommendation Systems","dataset":"GoodReads-Children","model":"HGN","rank_in_archive_order":1,"of":1,"metrics":{"Recall@10":"0.1263","nDCG@10":"0.113"},"uses_additional_data":false},{"leaderboard":"/sota/recommendation-systems-on-goodreads-comics","task":"Recommendation Systems","dataset":"GoodReads-Comics","model":"HGN","rank_in_archive_order":1,"of":1,"metrics":{"Recall@10":"0.1743","nDCG@10":"0.1927"},"uses_additional_data":false},{"leaderboard":"/sota/collaborative-filtering-on-movielens-20m","task":"Recommendation Systems","dataset":"MovieLens 20M","model":"HGN","rank_in_archive_order":4,"of":18,"metrics":{"Recall@10":"0.1255","nDCG@10":"0.1195"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1906.09217","atlas_url":"https://app.syntology.ai/?focus=1906.09217","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1906.09217"}},"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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