{"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/personalized-top-n-sequential-recommendation","title":"Personalized Top-N Sequential Recommendation via Convolutional Sequence Embedding","arxiv_id":"1809.07426","date":"2018-09-19","proceeding":null,"authors":["Jiaxi Tang","Ke Wang"],"abstract":"Top-$N$ sequential recommendation models each user as a sequence of items\ninteracted in the past and aims to predict top-$N$ ranked items that a user\nwill likely interact in a `near future'. The order of interaction implies that\nsequential patterns play an important role where more recent items in a\nsequence have a larger impact on the next item. In this paper, we propose a\nConvolutional Sequence Embedding Recommendation Model (\\emph{Caser}) as a\nsolution to address this requirement. The idea is to embed a sequence of recent\nitems into an `image' in the time and latent spaces and learn sequential\npatterns as local features of the image using convolutional filters. This\napproach provides a unified and flexible network structure for capturing both\ngeneral preferences and sequential patterns. The experiments on public datasets\ndemonstrated that Caser consistently outperforms state-of-the-art sequential\nrecommendation methods on a variety of common evaluation metrics.","url_abs":"http://arxiv.org/abs/1809.07426v1","url_pdf":"http://arxiv.org/pdf/1809.07426v1.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":"personalized-top-n-sequential-recommendation","repo_url":"https://github.com/LinJayan/Caser_Paddle","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"paddle","reach":{"status":"ok"}},{"paper_slug":"personalized-top-n-sequential-recommendation","repo_url":"https://github.com/UlionTse/mlgb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"personalized-top-n-sequential-recommendation","repo_url":"https://github.com/massquantity/LibRecommender","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"personalized-top-n-sequential-recommendation","repo_url":"https://github.com/graytowne/caser_pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"LGPL-3.0"}},{"paper_slug":"personalized-top-n-sequential-recommendation","repo_url":"https://github.com/microsoft/recommenders","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"sequential-recommendation","task_name":"Sequential Recommendation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.07426","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}