{"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/predicting-ratings-in-multi-criteria","title":"Predicting ratings in multi-criteria recommender systems via a collective factor model","arxiv_id":null,"date":"2021-05-21","proceeding":"Demal @ The Web Conference 2021 5","authors":["Ge Fan","Chaoyun Zhang","Junyang Chen","Kaishun Wu"],"abstract":"In a multi-criteria recommender system, users are allowed to give an overall rating to an item and provide a score on each of its attribute. Finding an effective method to exploit a user s multi-criteria ratings to predict the overall rating becomes one of the most important challenges. Among traditional solutions, most of the architectures are not designed in an end-to-end manner. These approaches initially estimate a user s multi-criteria scores, and train a separate model to predict the user s overall rating. This introduces extra training overhead, and the overall prediction accuracy is usually sensitive to its multi-criteria ratings models. In this paper, we propose a collective model to predict user s overall rating by automatically weighting each of the predicted multi-criteria sub-scores. The proposed architecture integrates the multi-criteria ratings and the overall rating models in a unified system, which allows to train and perform multi-criteria recommendation in an end-to-end manner. Experiments on 3 real datasets show that our proposed architectures achieve up to 13.14% lower prediction error over baseline approaches.","url_abs":"https://demalworkshop.github.io/www2021/papers/predictingratings.pdf","url_pdf":"https://demalworkshop.github.io/www2021/papers/predictingratings.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":"predicting-ratings-in-multi-criteria","repo_url":"https://github.com/LucaM1985/CFM","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"attribute","task_name":"Attribute"},{"task_slug":"recommendation-systems","task_name":"Recommendation Systems"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/recommendation-systems-on-beeradvocate","task":"Recommendation Systems","dataset":"BeerAdvocate","model":"CFM","rank_in_archive_order":1,"of":1,"metrics":{"MAE":"0.5833","RMSE":"0.5833"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}