{"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/cascading-bandits-for-large-scale","title":"Cascading Bandits for Large-Scale Recommendation Problems","arxiv_id":"1603.05359","date":"2016-03-17","proceeding":null,"authors":["Shi Zong","Hao Ni","Kenny Sung","Nan Rosemary Ke","Zheng Wen","Branislav Kveton"],"abstract":"Most recommender systems recommend a list of items. The user examines the\nlist, from the first item to the last, and often chooses the first attractive\nitem and does not examine the rest. This type of user behavior can be modeled\nby the cascade model. In this work, we study cascading bandits, an online\nlearning variant of the cascade model where the goal is to recommend $K$ most\nattractive items from a large set of $L$ candidate items. We propose two\nalgorithms for solving this problem, which are based on the idea of linear\ngeneralization. The key idea in our solutions is that we learn a predictor of\nthe attraction probabilities of items from their features, as opposing to\nlearning the attraction probability of each item independently as in the\nexisting work. This results in practical learning algorithms whose regret does\nnot depend on the number of items $L$. We bound the regret of one algorithm and\ncomprehensively evaluate the other on a range of recommendation problems. The\nalgorithm performs well and outperforms all baselines.","url_abs":"http://arxiv.org/abs/1603.05359v2","url_pdf":"http://arxiv.org/pdf/1603.05359v2.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":"cascading-bandits-for-large-scale","repo_url":"https://github.com/niravnb/Movie-Recommendation-using-Cascading-Bandits","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"multi-armed-bandits","task_name":"Multi-Armed Bandits"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"},{"task_slug":"thompson-sampling","task_name":"Thompson Sampling"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1603.05359","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}