{"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/temporal-learning-and-sequence-modeling-for-a","title":"Temporal Learning and Sequence Modeling for a Job Recommender System","arxiv_id":"1608.03333","date":"2016-08-11","proceeding":null,"authors":["Kuan Liu","Xing Shi","Anoop Kumar","Linhong Zhu","Prem Natarajan"],"abstract":"We present our solution to the job recommendation task for RecSys Challenge\n2016. The main contribution of our work is to combine temporal learning with\nsequence modeling to capture complex user-item activity patterns to improve job\nrecommendations. First, we propose a time-based ranking model applied to\nhistorical observations and a hybrid matrix factorization over time re-weighted\ninteractions. Second, we exploit sequence properties in user-items activities\nand develop a RNN-based recommendation model. Our solution achieved 5$^{th}$\nplace in the challenge among more than 100 participants. Notably, the strong\nperformance of our RNN approach shows a promising new direction in employing\nsequence modeling for recommendation systems.","url_abs":"http://arxiv.org/abs/1608.03333v1","url_pdf":"http://arxiv.org/pdf/1608.03333v1.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":"temporal-learning-and-sequence-modeling-for-a","repo_url":"https://github.com/skywaLKer518/A-Recsys","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}