{"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/adaptive-user-modeling-with-long-and-short","title":"Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation","arxiv_id":null,"date":"2019-01-01","proceeding":"IJCAI 2019 1","authors":["Zeping Yu","Jianxun Lian","Ahmad Mahmoody","Gongshen Liu","Xing Xie"],"abstract":"User modeling is an essential task for online rec- ommender systems. In the past few decades, col- laborative filtering (CF) techniques have been well studied to model users’ long term preferences. Recently, recurrent neural networks (RNN) have shown a great advantage in modeling users’ short term preference. A natural way to improve the rec- ommender is to combine both long-term and short- term modeling. Previous approaches neglect the importance of dynamically integrating these two user modeling paradigms. Moreover, users’ be- haviors are much more complex than sentences in language modeling or images in visual computing, thus the classical structures of RNN such as Long Short-Term Memory (LSTM) need to be upgraded for better user modeling. In this paper, we im- prove the traditional RNN structure by proposing a time-aware controller and a content-aware con- troller, so that contextual information can be well considered to control the state transition. We fur- ther propose an attention-based framework to com- bine users’ long-term and short-term preferences, thus users’ representation can be generated adap- tively according to the specific context. We con- duct extensive experiments on both public and in- dustrial datasets. The results demonstrate that our proposed method outperforms several state-of-art methods consistently.","url_abs":"https://www.ijcai.org/proceedings/2019/0585.pdf","url_pdf":"https://www.ijcai.org/proceedings/2019/0585.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":"adaptive-user-modeling-with-long-and-short","repo_url":"https://github.com/microsoft/recommenders","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"adaptive-user-modeling-with-long-and-short","repo_url":"https://github.com/zepingyu0512/sli_rec","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"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-product-data","task":"Recommendation Systems","dataset":"Amazon Product Data","model":"SLi-Rec","rank_in_archive_order":2,"of":2,"metrics":{"AUC":"0.8494","F1":"0.7745"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}