2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Collaborative Filtering Recommender Systems Using Tag Information Huizhi Liang, Yue Xu, Yuefeng Li, Richi Nayak Queensland University of Technology, Brisbane, australia oklianghuizi@gmail.com,lyue.xu,y2.li,r:nayak@qut.edu.au Abstract voting on the tagged information resources or items [1] Thus, the tagging information can be used to make Recommender Systems is one of the effective tools to recommendations deal with information overload issue. Similar with the Currently some researches are focusing on how to explicit rating and other implicit rating behaviours such use collaborative tagging information to recommend Is purchase behaviour, click streams, and browsing personalized tags to users [2], but not much work has history etc, the tagging information implies users been done on utilizing tagging information to help users important personal interests and preferences to find interested items easily and quickly. information, which can be used to recommend In this paper, we will discuss how to recommen personalized items to users. This paper is to explore items to users based on tag information. how to utilise tagging information to do personalized recommendations. Based on the distinctive three 2. Related work a new user profiling and similarity measure method is Collaborative filtering is a traditional and wildly used proposed. The experiments suggest that the proposed approach to recommend items to users based on the approach is better than the traditional collaborative assumption that similar minded people may have filtering recommender systems using only rating data. similar taste or behaviors. In general, there are two kinds of collaborative filtering methods: user-based and 1. Introduction item-based. Though there is a lot of work on the collaborative filtering recommender systems, to the best Recommender systems can provide personalized of our knowledge, only Tso-Sutter's [3] work discussed contents, services and information items to potential bout using the tag information to doitem consumers to decrease information retrieval time and recommendation 0-Sutter's work, nformation was explicit rating is not always available, the implicit converted into two 2-dimensional relationships, user-tag rating such as purchase history, downloading behaviour and tag-item, and was used as a supplementary source and click patterns etc. become another important to extend the rating data. Because it ignored the three information source for recommender systems dimensional relationship among users, items, and tags With the development of web 2.0, collaborative the users' tagging behavior was not accurately profiled, tagging information becomes popular. Besides helping and thus the recommendation quality based on the ser organize his or her personal collections, a tag also extended data is still not satisfactory can be regarded as a user's personal opinion expression while tagging can be considered as implicit rating or 3. Tag-based Recommender systems 978-0-7695-3496-1/08525.00◎2008IEEE DOI 101109/WIIAT200897Collaborative Filtering Recommender Systems Using Tag Information Huizhi Liang, Yue Xu, Yuefeng Li, Richi Nayak Faculty of Information Technology Queensland University of Technology, Brisbane, Australia oklianghuizi@gmail.com, {yue.xu, y2.li, r.nayak}@qut.edu.au Abstract Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviours such as purchase behaviour, click streams, and browsing history etc., the tagging information implies user’s important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data. 1. Introduction Recommender systems can provide personalized contents, services and information items to potential consumers to decrease information retrieval time and support decision making process. Because user’s explicit rating is not always available, the implicit rating such as purchase history, downloading behaviour and click patterns etc. become another important information source for recommender systems. With the development of web 2.0, collaborative tagging information becomes popular. Besides helping user organize his or her personal collections, a tag also can be regarded as a user’s personal opinion expression, while tagging can be considered as implicit rating or voting on the tagged information resources or items [1]. Thus, the tagging information can be used to make recommendations. Currently some researches are focusing on how to use collaborative tagging information to recommend personalized tags to users [2], but not much work has been done on utilizing tagging information to help users to find interested items easily and quickly. In this paper, we will discuss how to recommend items to users based on tag information. 2. Related work Collaborative filtering is a traditional and wildly used approach to recommend items to users based on the assumption that similar minded people may have similar taste or behaviors. In general, there are two kinds of collaborative filtering methods: user-based and item-based. Though there is a lot of work on the collaborative filtering recommender systems, to the best of our knowledge, only Tso-Sutter’s [3] work discussed about using the tag information to do item recommendation. In Tso-Sutter’s work, the tag information was converted into two 2-dimensional relationships, user-tag and tag-item, and was used as a supplementary source to extend the rating data. Because it ignored the three dimensional relationship among users, items, and tags, the users’ tagging behavior was not accurately profiled, and thus the recommendation quality based on the extended data is still not satisfactory. 3. Tag-based Recommender systems 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology 978-0-7695-3496-1/08 $25.00 © 2008 IEEE DOI 10.1109/WIIAT.2008.97 59 Authorized licensed use limited to: PORTLAND STATE UNIVERSITY. Downloaded on July 31, 2009 at 18:37 from IEEE Xplore. Restrictions apply