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| Clustering and Recommending Users Based on Tags and Relation Network |
Xiong Huixiang( ), Jiang Wuxuan |
| School of Information Management, Central China Normal University, Wuhan 430079, China |
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Abstract [Objective] This paper proposes a new model to recommend potential similar users with the help of social tags and relation network. [Methods] First, we explored characteristics of the users’ short or long-term interests based on the social tagging system. Then, we built a user-clustering model using multidimensional scaling method with the tags and relationship data. Finally, we recommended similar users based on the clustering results. The proposed model was examined with Weibo data. [Results] We found that the new model could effectively combine the characteristics of the user’s interests, and then identify the potential similar ones. [Limitations] The sample data does not include everything on user interests. Thus, we only examined the effectiveness of the proposed model with limited data. [Conclusions] The user recommendation model based on static tags and dynamic relational network could improve the personalized recommendation services.
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Received: 07 April 2017
Published: 25 August 2017
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