Abstract
The accurate identification of user interests on Twitter can lead to more efficient procurement of targeted content for the users. While the analysis of user content has engaged with on Twitter is a rich source for detecting the user’s interests, prior research have shown that it may not be sufficient. There have been work that attempt to identify a user’s implicit interests, i.e., those topics that could interest the user but the user has not engaged with them in the past. Prior work has shown that topic semantic relatedness is an important feature for determining users’ implicit interests. In this paper, we explore the possibility of identifying users’ implicit interests solely based on topic association through frequent pattern mining without regard for the semantics of the topics. We show in our experiments that topic association is a strong feature for determining users’ implicit interests.
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Trikha, A.K., Zarrinkalam, F., Bagheri, E. (2018). Topic-Association Mining for User Interest Detection. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds) Advances in Information Retrieval. ECIR 2018. Lecture Notes in Computer Science(), vol 10772. Springer, Cham. https://doi.org/10.1007/978-3-319-76941-7_60
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DOI: https://doi.org/10.1007/978-3-319-76941-7_60
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