{"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/from-amateurs-to-connoisseurs-modeling-the","title":"From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews","arxiv_id":"1303.4402","date":"2013-03-18","proceeding":null,"authors":["Julian McAuley","Jure Leskovec"],"abstract":"Recommending products to consumers means not only understanding their tastes,\nbut also understanding their level of experience. For example, it would be a\nmistake to recommend the iconic film Seven Samurai simply because a user enjoys\nother action movies; rather, we might conclude that they will eventually enjoy\nit -- once they are ready. The same is true for beers, wines, gourmet foods --\nor any products where users have acquired tastes: the `best' products may not\nbe the most `accessible'. Thus our goal in this paper is to recommend products\nthat a user will enjoy now, while acknowledging that their tastes may have\nchanged over time, and may change again in the future. We model how tastes\nchange due to the very act of consuming more products -- in other words, as\nusers become more experienced. We develop a latent factor recommendation system\nthat explicitly accounts for each user's level of experience. We find that such\na model not only leads to better recommendations, but also allows us to study\nthe role of user experience and expertise on a novel dataset of fifteen million\nbeer, wine, food, and movie reviews.","url_abs":"http://arxiv.org/abs/1303.4402v1","url_pdf":"http://arxiv.org/pdf/1303.4402v1.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":[],"tasks":[],"methods":[],"datasets_introduced":[{"slug":"amazon-fine-foods","name":"Amazon Fine Foods","full_name":""},{"slug":"beeradvocate","name":"BeerAdvocate","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1303.4402","atlas_url":"https://app.syntology.ai/?focus=1303.4402","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}