TagiCoFi: tag informed collaborative filtering
TagiCoFi: tag informed collaborative filtering
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DOI：
10.1145/1639714.1639727
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发表时间：
2009-10
期刊：
影响因子：
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通讯作者：
Yi Zhen;Wubin Li;D. Yeung
中科院分区：
文献类型：
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作者：
Yi Zhen;Wubin Li;D. Yeung
除了评级信息，越来越多的现代推荐系统还允许用户为商品添加个性化标签。这样的标签信息可以为项目推荐提供非常有用的信息，因为用户对项目的兴趣可以通过他们经常使用的标签隐含地反映出来。虽然最近一些基于内容的推荐系统已经初步尝试利用标签信息来提高推荐性能，但很少有基于协同过滤的推荐系统利用标签信息来帮助项目推荐过程。在本文中，我们提出了一种新的框架，称为标签信息协同过滤(TagiCoFi)，将标签信息无缝地整合到协同过滤过程中。实验结果表明，TagiCoFi的性能优于TagiCoFi，后者即使在标签信息可用的情况下也会将其丢弃，并达到了最先进的性能。
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.