Abstract
Mining user interests and preference plays an important role for many applications such as information retrieval and recommender systems. This paper intends to study how to infer interests for new users and inactive users from social media. Although some recently proposed methods can mine user interests efficiently, these works cannot make full use of relationship between users in their social network. In this paper, we propose a novel approach to infer interests of new users or inactive users based on social connections between users. A random-walk based mutual reinforcement model combining both text and link information is developed in the approach. More importantly, we compare the contribution of different social connections such as “follow”, “retweet”, “mention”, and “comment” to interest sharing. Experiments conducted on real dataset show that our method is effective and outperforms existing algorithms, and different social connections have different impacts on mining user interests.
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Wang, T., Liu, H., He, J., Du, X. (2013). Mining User Interests from Information Sharing Behaviors in Social Media. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7819. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37456-2_8
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DOI: https://doi.org/10.1007/978-3-642-37456-2_8
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