{"title":"数字社交媒体中的客观评价还是主观评价:对网络用户电影评分的进一步理解","authors":"Zhenfei Feng, Laurence Favier","doi":"10.1145/3240117.3240127","DOIUrl":null,"url":null,"abstract":"Recommender systems exploit users' ratings to infer their preferences in order to propose recommendations of items that match up with users' interests. Recently, online movie review sites have combined RS and social media. However, in these sites, ratings can have different meaning for users: users may choose one criterion or another. Thus, users' ratings might not be reliable enough to infer their preference in this context. In this study, we find that users may rate movie either by referring to perceived movie quality and/or by referring to their own preferences. Furthermore, users chose different criteria in different contexts. We discuss how users' ratings can be interpreted more precisely in order to improve the performance of recommender systems.","PeriodicalId":318568,"journal":{"name":"Proceedings of the 1st International Conference on Digital Tools & Uses Congress","volume":"65 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Objective Evaluation or Subjective Evaluation in Digital Social Media: A further understanding of online user's rating of movies\",\"authors\":\"Zhenfei Feng, Laurence Favier\",\"doi\":\"10.1145/3240117.3240127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recommender systems exploit users' ratings to infer their preferences in order to propose recommendations of items that match up with users' interests. Recently, online movie review sites have combined RS and social media. However, in these sites, ratings can have different meaning for users: users may choose one criterion or another. Thus, users' ratings might not be reliable enough to infer their preference in this context. In this study, we find that users may rate movie either by referring to perceived movie quality and/or by referring to their own preferences. Furthermore, users chose different criteria in different contexts. We discuss how users' ratings can be interpreted more precisely in order to improve the performance of recommender systems.\",\"PeriodicalId\":318568,\"journal\":{\"name\":\"Proceedings of the 1st International Conference on Digital Tools & Uses Congress\",\"volume\":\"65 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 1st International Conference on Digital Tools & Uses Congress\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3240117.3240127\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 1st International Conference on Digital Tools & Uses Congress","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3240117.3240127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Objective Evaluation or Subjective Evaluation in Digital Social Media: A further understanding of online user's rating of movies
Recommender systems exploit users' ratings to infer their preferences in order to propose recommendations of items that match up with users' interests. Recently, online movie review sites have combined RS and social media. However, in these sites, ratings can have different meaning for users: users may choose one criterion or another. Thus, users' ratings might not be reliable enough to infer their preference in this context. In this study, we find that users may rate movie either by referring to perceived movie quality and/or by referring to their own preferences. Furthermore, users chose different criteria in different contexts. We discuss how users' ratings can be interpreted more precisely in order to improve the performance of recommender systems.