{"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/modeling-user-exposure-in-recommendation","title":"Modeling User Exposure in Recommendation","arxiv_id":"1510.07025","date":"2015-10-23","proceeding":null,"authors":["Dawen Liang","Laurent Charlin","James McInerney","David M. Blei"],"abstract":"Collaborative filtering analyzes user preferences for items (e.g., books,\nmovies, restaurants, academic papers) by exploiting the similarity patterns\nacross users. In implicit feedback settings, all the items, including the ones\nthat a user did not consume, are taken into consideration. But this assumption\ndoes not accord with the common sense understanding that users have a limited\nscope and awareness of items. For example, a user might not have heard of a\ncertain paper, or might live too far away from a restaurant to experience it.\nIn the language of causal analysis, the assignment mechanism (i.e., the items\nthat a user is exposed to) is a latent variable that may change for various\nuser/item combinations. In this paper, we propose a new probabilistic approach\nthat directly incorporates user exposure to items into collaborative filtering.\nThe exposure is modeled as a latent variable and the model infers its value\nfrom data. In doing so, we recover one of the most successful state-of-the-art\napproaches as a special case of our model, and provide a plug-in method for\nconditioning exposure on various forms of exposure covariates (e.g., topics in\ntext, venue locations). We show that our scalable inference algorithm\noutperforms existing benchmarks in four different domains both with and without\nexposure covariates.","url_abs":"http://arxiv.org/abs/1510.07025v2","url_pdf":"http://arxiv.org/pdf/1510.07025v2.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":"modeling-user-exposure-in-recommendation","repo_url":"https://github.com/dawenl/expo-mf","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"collaborative-filtering","task_name":"Collaborative Filtering"},{"task_slug":"common-sense-reasoning","task_name":"Common Sense Reasoning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1510.07025","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}