Fang Wang



This paper introduces an approach to organising multi-interest communities in which a user may belong to more than one community. The interests of a user are first identified from the resources he handled and then refined through interest association analysis in order to remove false or redundant interests. To each identified interest topic, users who have this topic are clustered together, so a series of multi-interest communities are formed. Because members of a community may have interest in the topics of other communities, the formed communities are also connected with each other, resulting in a kind of community network indicating interest associations of groups of users. Based on formed multi-interest communities, users will receive useful recommendations within their own communities and from other related communities. This provides users opportunities to obtain information beyond their current interests so new interests of the users may be discovered. The multi-interest communities approach has been examined on the EachMovie data. The experimental results showed that the formed multi-interest communities were more cohesive and condensed when users were clustered according to their refined interest topics. The users also received much more recommendations based on multi-interest communities.


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Paper Citation

in Harvard Style

Wang F. (2007). MULTI-INTEREST COMMUNITIES AND COMMUNITY-BASED RECOMMENDATION . In Proceedings of the Third International Conference on Web Information Systems and Technologies - Volume 3: WEBIST, ISBN 978-972-8865-79-5, pages 37-45. DOI: 10.5220/0001273800370045

in Bibtex Style

author={Fang Wang},
booktitle={Proceedings of the Third International Conference on Web Information Systems and Technologies - Volume 3: WEBIST,},

in EndNote Style

JO - Proceedings of the Third International Conference on Web Information Systems and Technologies - Volume 3: WEBIST,
SN - 978-972-8865-79-5
AU - Wang F.
PY - 2007
SP - 37
EP - 45
DO - 10.5220/0001273800370045