Nan Zheng, Qiudan Li, Shengcai Liao, Leiming Zhang
{"title":"Flickr group recommendation based on tensor decomposition","authors":"Nan Zheng, Qiudan Li, Shengcai Liao, Leiming Zhang","doi":"10.1145/1835449.1835591","DOIUrl":null,"url":null,"abstract":"Over the last few years, Flickr has gained massive popularity and groups in Flickr are one of the main ways for photo diffusion. However, the huge volume of groups brings troubles for users to decide which group to choose. In this paper, we propose a tensor decomposition-based group recommendation model to suggest groups to users which can help tackle this problem. The proposed model measures the latent associations between users and groups by considering both semantic tags and social relations. Experimental results show the usefulness of the proposed model.","PeriodicalId":378368,"journal":{"name":"Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"45","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/1835449.1835591","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 45
Abstract
Over the last few years, Flickr has gained massive popularity and groups in Flickr are one of the main ways for photo diffusion. However, the huge volume of groups brings troubles for users to decide which group to choose. In this paper, we propose a tensor decomposition-based group recommendation model to suggest groups to users which can help tackle this problem. The proposed model measures the latent associations between users and groups by considering both semantic tags and social relations. Experimental results show the usefulness of the proposed model.