{"title":"Iterative Neighbourhood Similarity Computation for Collaborative Filtering","authors":"Yun Zhang, Peter M. Andreae","doi":"10.1109/WIIAT.2008.222","DOIUrl":null,"url":null,"abstract":"Collaborative filtering recommender systems make predictions based on the preferences of users considered like-minded to the target user (user-based), or the popularities of items similar to the target item (item-based). There have been several approaches of combining user-based and item-based collaborative filtering. However, they are predominantly along the lines of averaging user-based and item-based predictions in a close-to-linear fashion, thus behave like smoothing mechanisms and only work well on sparse datasets. This article proposes a new way of combining user and item based collaborative filtering in a nonlinear fashion. The goal of the approach is to improve recommendation accuracy on regular datasets, by means of a more sensible neighbourhood similarity computation method that guides the user similarity computation using the itemspsila similarities to the item that is being predicted.","PeriodicalId":393772,"journal":{"name":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2008-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WIIAT.2008.222","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4
Abstract
Collaborative filtering recommender systems make predictions based on the preferences of users considered like-minded to the target user (user-based), or the popularities of items similar to the target item (item-based). There have been several approaches of combining user-based and item-based collaborative filtering. However, they are predominantly along the lines of averaging user-based and item-based predictions in a close-to-linear fashion, thus behave like smoothing mechanisms and only work well on sparse datasets. This article proposes a new way of combining user and item based collaborative filtering in a nonlinear fashion. The goal of the approach is to improve recommendation accuracy on regular datasets, by means of a more sensible neighbourhood similarity computation method that guides the user similarity computation using the itemspsila similarities to the item that is being predicted.