{"title":"评分时你会受到别人的影响吗?:通过整合模型改进评级预测","authors":"Yiming Liu, Xuezhi Cao, Yong Yu","doi":"10.1145/2959100.2959141","DOIUrl":null,"url":null,"abstract":"Conformity has a strong influence to user behaviors, even in online environment. When surfing online, users are usually flooded with others' opinions. These opinions implicitly contribute to the user's ongoing behaviors. However, there is no research work modeling online conformity yet. In this paper, we model user's conformity in online rating sites. We conduct analysis using real data to show the existence and strength of conformity in these scenarios. We propose a matrix-factorization-based conformity modeling technique to improve the accuracy of rating prediction. Experiments show that our model outperforms existing works significantly (with a relative improvement of 11.72% on RMSE). Therefore, we draw the conclusion that conformity modeling is important for understanding user behaviors and can contribute to further improve the online recommender systems.","PeriodicalId":315651,"journal":{"name":"Proceedings of the 10th ACM Conference on Recommender Systems","volume":"116 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"52","resultStr":"{\"title\":\"Are You Influenced by Others When Rating?: Improve Rating Prediction by Conformity Modeling\",\"authors\":\"Yiming Liu, Xuezhi Cao, Yong Yu\",\"doi\":\"10.1145/2959100.2959141\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Conformity has a strong influence to user behaviors, even in online environment. When surfing online, users are usually flooded with others' opinions. These opinions implicitly contribute to the user's ongoing behaviors. However, there is no research work modeling online conformity yet. In this paper, we model user's conformity in online rating sites. We conduct analysis using real data to show the existence and strength of conformity in these scenarios. We propose a matrix-factorization-based conformity modeling technique to improve the accuracy of rating prediction. Experiments show that our model outperforms existing works significantly (with a relative improvement of 11.72% on RMSE). Therefore, we draw the conclusion that conformity modeling is important for understanding user behaviors and can contribute to further improve the online recommender systems.\",\"PeriodicalId\":315651,\"journal\":{\"name\":\"Proceedings of the 10th ACM Conference on Recommender Systems\",\"volume\":\"116 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"52\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 10th ACM Conference on Recommender Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2959100.2959141\",\"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 10th ACM Conference on Recommender Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2959100.2959141","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Are You Influenced by Others When Rating?: Improve Rating Prediction by Conformity Modeling
Conformity has a strong influence to user behaviors, even in online environment. When surfing online, users are usually flooded with others' opinions. These opinions implicitly contribute to the user's ongoing behaviors. However, there is no research work modeling online conformity yet. In this paper, we model user's conformity in online rating sites. We conduct analysis using real data to show the existence and strength of conformity in these scenarios. We propose a matrix-factorization-based conformity modeling technique to improve the accuracy of rating prediction. Experiments show that our model outperforms existing works significantly (with a relative improvement of 11.72% on RMSE). Therefore, we draw the conclusion that conformity modeling is important for understanding user behaviors and can contribute to further improve the online recommender systems.