{"title":"基于云模型聚类的多指标项目评价协同过滤推荐算法","authors":"Li Sa","doi":"10.1109/BCGIN.2011.170","DOIUrl":null,"url":null,"abstract":"Collaborative filtering recommendation algorithm is a personalized recommendation algorithm that is used widely in e-commerce recommendation system. In this paper, a collaborative filtering recomendation algorithm based on cloud model clustering of multi-indicators item evaluation is proposed. In the algorithm, the item evaluation is the object, time weighted function is introduced to item evaluation, soft culsters item based on cloud model and gets the recommended items. The algorithm solves problems of data updating and history validity of evaluation in the collaborative filtering algorithm. Soft cluster item based on cloud model is achieved to avoid the defects bringed by hard division.","PeriodicalId":127523,"journal":{"name":"2011 International Conference on Business Computing and Global Informatization","volume":"234 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Collaborative Filtering Recommendation Algorithm Based on Cloud Model Clustering of Multi-indicators Item Evaluation\",\"authors\":\"Li Sa\",\"doi\":\"10.1109/BCGIN.2011.170\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Collaborative filtering recommendation algorithm is a personalized recommendation algorithm that is used widely in e-commerce recommendation system. In this paper, a collaborative filtering recomendation algorithm based on cloud model clustering of multi-indicators item evaluation is proposed. In the algorithm, the item evaluation is the object, time weighted function is introduced to item evaluation, soft culsters item based on cloud model and gets the recommended items. The algorithm solves problems of data updating and history validity of evaluation in the collaborative filtering algorithm. Soft cluster item based on cloud model is achieved to avoid the defects bringed by hard division.\",\"PeriodicalId\":127523,\"journal\":{\"name\":\"2011 International Conference on Business Computing and Global Informatization\",\"volume\":\"234 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-07-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 International Conference on Business Computing and Global Informatization\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BCGIN.2011.170\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 International Conference on Business Computing and Global Informatization","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BCGIN.2011.170","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Collaborative Filtering Recommendation Algorithm Based on Cloud Model Clustering of Multi-indicators Item Evaluation
Collaborative filtering recommendation algorithm is a personalized recommendation algorithm that is used widely in e-commerce recommendation system. In this paper, a collaborative filtering recomendation algorithm based on cloud model clustering of multi-indicators item evaluation is proposed. In the algorithm, the item evaluation is the object, time weighted function is introduced to item evaluation, soft culsters item based on cloud model and gets the recommended items. The algorithm solves problems of data updating and history validity of evaluation in the collaborative filtering algorithm. Soft cluster item based on cloud model is achieved to avoid the defects bringed by hard division.