{"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/neural-att-entive-session-based","title":"Neural Att entive Session-based Recommendation","arxiv_id":null,"date":"2017-11-03","proceeding":"CIKM 2017 11","authors":["Jing Li","Pengjie Ren","Zhumin Chen","Zhaochun Ren","Tao Lian","Jun Ma"],"abstract":"Given e-commerce scenarios that user profiles are invisible, sessionbased recommendation is proposed to generate recommendation\r\nresults from short sessions. Previous work only considers the\r\nuser’s sequential behavior in the current session, whereas the\r\nuser’s main purpose in the current session is not emphasized. In\r\nthis paper, we propose a novel neural networks framework, i.e.,\r\nNeural Attentive Recommendation Machine (NARM), to tackle\r\nthis problem. Specifically, we explore a hybrid encoder with an\r\nattention mechanism to model the user’s sequential behavior and\r\ncapture the user’s main purpose in the current session, which\r\nare combined as a unified session representation later. We then\r\ncompute the recommendation scores for each candidate item with\r\na bi-linear matching scheme based on this unified session representation. We train NARM by jointly learning the item and session\r\nrepresentations as well as their matchings. We carried out extensive experiments on two benchmark datasets. Our experimental\r\nresults show that NARM outperforms state-of-the-art baselines on\r\nboth datasets. Furthermore, we also find that NARM achieves a\r\nsignificant improvement on long sessions, which demonstrates its\r\nadvantages in modeling the user’s sequential behavior and main\r\npurpose simultaneously","url_abs":"https://arxiv.org/abs/1711.04725","url_pdf":"https://arxiv.org/pdf/1711.04725.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":"neural-att-entive-session-based","repo_url":"https://github.com/Wang-Shuo/Neural-Attentive-Session-Based-Recommendation-PyTorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"session-based-recommendations","task_name":"Session-Based Recommendations"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}