{"title":"基于多级关联规则的推荐算法","authors":"Choonho Kim, Juntae Kim","doi":"10.1109/WI.2003.1241257","DOIUrl":null,"url":null,"abstract":"Recommendation systems predict user's preference to suggest items. Collaborative filtering is the most popular method in implementing a recommendation system. The collaborative filtering method computes similarities between users based on each user's known preference, and recommends the items preferred by similar users. Although the collaborative filtering method generally shows good performance, it suffers from two major problems - data sparseness and scalability. We present a model-based recommendation algorithm that uses multilevel association rules to alleviate those problems. In this algorithm, we build a model for preference prediction by using association rule mining. Multilevel association rules are used to compute preferences for items. The experimental results show that applying multilevel association rules is effective, and performance of the algorithm is improved compared with the collaborative filtering method in terms of the recall and the computation time.","PeriodicalId":403574,"journal":{"name":"Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2003-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"73","resultStr":"{\"title\":\"A recommendation algorithm using multi-level association rules\",\"authors\":\"Choonho Kim, Juntae Kim\",\"doi\":\"10.1109/WI.2003.1241257\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Recommendation systems predict user's preference to suggest items. Collaborative filtering is the most popular method in implementing a recommendation system. The collaborative filtering method computes similarities between users based on each user's known preference, and recommends the items preferred by similar users. Although the collaborative filtering method generally shows good performance, it suffers from two major problems - data sparseness and scalability. We present a model-based recommendation algorithm that uses multilevel association rules to alleviate those problems. In this algorithm, we build a model for preference prediction by using association rule mining. Multilevel association rules are used to compute preferences for items. The experimental results show that applying multilevel association rules is effective, and performance of the algorithm is improved compared with the collaborative filtering method in terms of the recall and the computation time.\",\"PeriodicalId\":403574,\"journal\":{\"name\":\"Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)\",\"volume\":\"44 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2003-10-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"73\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings IEEE/WIC International Conference on Web Intelligence (WI 2003)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WI.2003.1241257\",\"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 IEEE/WIC International Conference on Web Intelligence (WI 2003)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WI.2003.1241257","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A recommendation algorithm using multi-level association rules
Recommendation systems predict user's preference to suggest items. Collaborative filtering is the most popular method in implementing a recommendation system. The collaborative filtering method computes similarities between users based on each user's known preference, and recommends the items preferred by similar users. Although the collaborative filtering method generally shows good performance, it suffers from two major problems - data sparseness and scalability. We present a model-based recommendation algorithm that uses multilevel association rules to alleviate those problems. In this algorithm, we build a model for preference prediction by using association rule mining. Multilevel association rules are used to compute preferences for items. The experimental results show that applying multilevel association rules is effective, and performance of the algorithm is improved compared with the collaborative filtering method in terms of the recall and the computation time.