Abstract
A novel method for personalized tweet recommendation based on Field-aware Factorization Machines (FFMs) with adaptive field organization is presented in this paper. The proposed method realizes accurate recommendation of tweets in which users are interested by the following two contributions. First, sentiment factors such as opinions, thoughts and feelings included in tweets are newly introduced into FFMs in addition to their publisher and topic factors. Second, the proposed method newly enables adaptive organization of fields via canonical correlation analysis for multiple features extracted from each tweet. Experimental results for real-world datasets confirm the performance improvement of personalized tweet recommendation through the two contributions.
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