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TagiCoFi: Tag Informed Collaborative Filtering
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@MISC{Zhen_tagicofi:tag,
author = {Yi Zhen and Wu-jun Li and Dit-yan Yeung},
title = {TagiCoFi: Tag Informed Collaborative Filtering},
year = {}
}
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Abstract
Besides the rating information, an increasing number of modern recommender systems also allow the users to add personalized tags to the items. Such tagging information may provide very useful information for item recommendation, because the users ’ interests in items can be implicitly reflected by the tags that they often use. Although some content-based recommender systems have made preliminary attempts recently to utilize tagging information to improve the recommendation performance, few recommender systems based on collaborative filtering (CF) have employed tagging information to help the item recommendation procedure. In this paper, we propose a novel framework, called tag informed collaborative filtering (TagiCoFi), to seamlessly integrate tagging information into the CF procedure. Experimental results demonstrate that TagiCoFi outperforms its counterpart which discards the tagging information even when it is available, and achieves state-of-the-art performance.
Keyphrases
tag informed collaborative filtering collaborative filtering modern recommender system rating information preliminary attempt content-based recommender system state-of-the-art performance user interest item recommendation procedure useful information recommender system item recommendation personalized tag novel framework cf procedure recommendation performance experimental result tagging information
