{"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/ss4rec-continuous-time-sequential","title":"SS4Rec: Continuous-Time Sequential Recommendation with State Space Models","arxiv_id":"2502.08132","date":"2025-02-12","proceeding":null,"authors":["Wei Xiao","Huiying Wang","Qifeng Zhou"],"abstract":"Sequential recommendation is a key area in the field of recommendation systems aiming to model user interest based on historical interaction sequences with irregular intervals. While previous recurrent neural network-based and attention-based approaches have achieved significant results, they have limitations in capturing system continuity due to the discrete characteristics. In the context of continuous-time modeling, state space model (SSM) offers a potential solution, as it can effectively capture the dynamic evolution of user interest over time. However, existing SSM-based approaches ignore the impact of irregular time intervals within historical user interactions, making it difficult to model complexed user-item transitions in sequences. To address this issue, we propose a hybrid SSM-based model called SS4Rec for continuous-time sequential recommendation. SS4Rec integrates a time-aware SSM to handle irregular time intervals and a relation-aware SSM to model contextual dependencies, enabling it to infer user interest from both temporal and sequential perspectives. In the training process, the time-aware SSM and the relation-aware SSM are discretized by variable stepsizes according to user interaction time intervals and input data, respectively. This helps capture the continuous dependency from irregular time intervals and provides time-specific personalized recommendations. Experimental studies on five benchmark datasets demonstrate the superiority and effectiveness of SS4Rec.","url_abs":"https://arxiv.org/abs/2502.08132v2","url_pdf":"https://arxiv.org/pdf/2502.08132v2.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":"ss4rec-continuous-time-sequential","repo_url":"https://github.com/XiaoWei-i/SS4Rec","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"sequential-recommendation","task_name":"Sequential Recommendation"},{"task_slug":"state-space-models","task_name":"State Space Models"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/sequential-recommendation-on-amazon-sports","task":"Sequential Recommendation","dataset":"Amazon-Sports","model":"SS4Rec","rank_in_archive_order":1,"of":3,"metrics":{"HR@10":"0.1042","MRR@10":"0.0830","NDCG@10":"0.0880"},"uses_additional_data":false},{"leaderboard":"/sota/sequential-recommendation-on-amazon-video","task":"Sequential Recommendation","dataset":"Amazon-Video-Games","model":"SS4Rec","rank_in_archive_order":1,"of":1,"metrics":{"HR@10":"0.1362","MRR@10":"0.0678","NDCG@10":"0.0838"},"uses_additional_data":false},{"leaderboard":"/sota/sequential-recommendation-on-movielens-1m","task":"Sequential Recommendation","dataset":"MovieLens 1M","model":"SS4Rec","rank_in_archive_order":4,"of":4,"metrics":{"HR@10":"0.3561","MRR@10":"0.1688","NDCG@10":"0.2127"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}