{"title":"Comparing accuracy of cosine-based similarity and correlation-based similarity algorithms in tourism recommender systems","authors":"Elnaz Bigdeli, Z. Bahmani","doi":"10.1109/ICMIT.2008.4654410","DOIUrl":null,"url":null,"abstract":"Recommender system has a long history as a successful application in artificial intelligence. A growth in the number of products, which has been offered by different e-commerce platforms, leads to a technology which can help customers to choose and buy products. Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. This paper describes some algorithms designed for this task including cosine-based similarity algorithm and correlation-based similarity algorithm. The predictive accuracy of various methods in tourism recommender domains is compared. On the other hand, we have designed and implemented a recommender system in e-tourism in order to compare performance of these algorithms. Finally, we conclude that correlation based similarity algorithm acts better than Cosine based similarity algorithm.","PeriodicalId":332967,"journal":{"name":"2008 4th IEEE International Conference on Management of Innovation and Technology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 4th IEEE International Conference on Management of Innovation and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICMIT.2008.4654410","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 21
Abstract
Recommender system has a long history as a successful application in artificial intelligence. A growth in the number of products, which has been offered by different e-commerce platforms, leads to a technology which can help customers to choose and buy products. Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. This paper describes some algorithms designed for this task including cosine-based similarity algorithm and correlation-based similarity algorithm. The predictive accuracy of various methods in tourism recommender domains is compared. On the other hand, we have designed and implemented a recommender system in e-tourism in order to compare performance of these algorithms. Finally, we conclude that correlation based similarity algorithm acts better than Cosine based similarity algorithm.