{"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-context-enabled-recurrent-neural","title":"Hierarchical Context enabled Recurrent Neural Network for Recommendation","arxiv_id":"1904.12674","date":"2019-04-26","proceeding":null,"authors":["Kyungwoo Song","Mingi Ji","Sungrae Park","Il-Chul Moon"],"abstract":"A long user history inevitably reflects the transitions of personal interests\nover time. The analyses on the user history require the robust sequential model\nto anticipate the transitions and the decays of user interests. The user\nhistory is often modeled by various RNN structures, but the RNN structures in\nthe recommendation system still suffer from the long-term dependency and the\ninterest drifts. To resolve these challenges, we suggest HCRNN with three\nhierarchical contexts of the global, the local, and the temporary interests.\nThis structure is designed to withhold the global long-term interest of users,\nto reflect the local sub-sequence interests, and to attend the temporary\ninterests of each transition. Besides, we propose a hierarchical context-based\ngate structure to incorporate our \\textit{interest drift assumption}. As we\nsuggest a new RNN structure, we support HCRNN with a complementary\n\\textit{bi-channel attention} structure to utilize hierarchical context. We\nexperimented the suggested structure on the sequential recommendation tasks\nwith CiteULike, MovieLens, and LastFM, and our model showed the best\nperformances in the sequential recommendations.","url_abs":"http://arxiv.org/abs/1904.12674v1","url_pdf":"http://arxiv.org/pdf/1904.12674v1.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-context-enabled-recurrent-neural","repo_url":"https://github.com/gtshs2/HCRNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"sequential-recommendation","task_name":"Sequential Recommendation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.12674","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}