Neighborhood-User Profiling Based on Perception Relationship in the Micro-blog Scenario

Jianxing Zheng, Bofeng Zhanh, Xiaodong Yue, Guobing Zou, Jianhua Ma, Keyuan Jiang


In the micro-blog scenario, personal user profiling relying on content is limited for recommending desired diverse subjects due to its shortcomings of short text, often leading to a poor recall. Currently, many methods only utilized the personal knowledge from each individual user to represent user profile without considering the neighborhood information. However, resource information related to neighboring friends play an important role in improving the performance of recommender systems. In this paper, we present the personalized expanded user profiling for micro-blog subject recommendation via ontology semantics structure. Next, taking into account diffusion ability of followee friends, we discuss resource perception relationship (RPR)
and follow perception relationship (FPR). Finally, we discuss how, by adjusting the importance of RPR and FPR, the neighborhood is selected to construct neighborhood-user profile, which can mine new relevant subjects for target user. Our experimental results demonstrate the effectiveness of our neighborhood-user profiling in comparison to the existing collaborative filtering and personal user profile recommendation approaches on Sina micro-blog platform datasets.

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