{"title":"基于记忆的协同推荐系统的模糊加权相似度度量","authors":"Mohammad Yahya H. Al-Shamri, N. Al-Ashwal","doi":"10.4236/JILSA.2014.61001","DOIUrl":null,"url":null,"abstract":"Memory-based collaborative \nrecommender system (CRS) computes the similarity between users based on their \ndeclared ratings. However, not all ratings are of the same importance to the \nuser. The set of ratings each user weights highly differs from user to user \naccording to his mood and taste. This is usually reflected in the user’s rating \nscale. Accordingly, many efforts have been done to introduce weights to the \nsimilarity measures of CRSs. This paper proposes fuzzy weightings for the most \ncommon similarity measures for memory-based CRSs. Fuzzy weighting can be \nconsidered as a learning mechanism for capturing the preferences of users for \nratings. Comparing with genetic algorithm learning, fuzzy weighting is fast, \neffective and does not require any more space. Moreover, fuzzy weightings based \non the rating deviations from the user’s mean of ratings take into account the \ndifferent rating scales of different users. The experimental results \nshow that fuzzy weightings obviously improve the CRSs performance to a good \nextent.","PeriodicalId":69452,"journal":{"name":"智能学习系统与应用(英文)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2014-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4236/JILSA.2014.61001","citationCount":"23","resultStr":"{\"title\":\"Fuzzy-Weighted Similarity Measures for Memory-Based Collaborative Recommender Systems\",\"authors\":\"Mohammad Yahya H. Al-Shamri, N. Al-Ashwal\",\"doi\":\"10.4236/JILSA.2014.61001\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Memory-based collaborative \\nrecommender system (CRS) computes the similarity between users based on their \\ndeclared ratings. However, not all ratings are of the same importance to the \\nuser. The set of ratings each user weights highly differs from user to user \\naccording to his mood and taste. This is usually reflected in the user’s rating \\nscale. Accordingly, many efforts have been done to introduce weights to the \\nsimilarity measures of CRSs. This paper proposes fuzzy weightings for the most \\ncommon similarity measures for memory-based CRSs. Fuzzy weighting can be \\nconsidered as a learning mechanism for capturing the preferences of users for \\nratings. Comparing with genetic algorithm learning, fuzzy weighting is fast, \\neffective and does not require any more space. Moreover, fuzzy weightings based \\non the rating deviations from the user’s mean of ratings take into account the \\ndifferent rating scales of different users. The experimental results \\nshow that fuzzy weightings obviously improve the CRSs performance to a good \\nextent.\",\"PeriodicalId\":69452,\"journal\":{\"name\":\"智能学习系统与应用(英文)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-01-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.4236/JILSA.2014.61001\",\"citationCount\":\"23\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"智能学习系统与应用(英文)\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.4236/JILSA.2014.61001\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"智能学习系统与应用(英文)","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.4236/JILSA.2014.61001","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Fuzzy-Weighted Similarity Measures for Memory-Based Collaborative Recommender Systems
Memory-based collaborative
recommender system (CRS) computes the similarity between users based on their
declared ratings. However, not all ratings are of the same importance to the
user. The set of ratings each user weights highly differs from user to user
according to his mood and taste. This is usually reflected in the user’s rating
scale. Accordingly, many efforts have been done to introduce weights to the
similarity measures of CRSs. This paper proposes fuzzy weightings for the most
common similarity measures for memory-based CRSs. Fuzzy weighting can be
considered as a learning mechanism for capturing the preferences of users for
ratings. Comparing with genetic algorithm learning, fuzzy weighting is fast,
effective and does not require any more space. Moreover, fuzzy weightings based
on the rating deviations from the user’s mean of ratings take into account the
different rating scales of different users. The experimental results
show that fuzzy weightings obviously improve the CRSs performance to a good
extent.