Sentiment-aware personalized tweet recommendation through multimodal FFM

For realizing quick and accurate access to desired information and ef- fective advertisements or election campaigns, personalized tweet recommendation is highly demanded. Since multimedia contents including tweets are tools for users to convey their sentiment, users’ interest in tweets is strongly influenced by sen- timent factors. Therefore, successful personalized tweet recommendation can be realized if sentiment in tweets can be estimated. However, sentiment factors were not taken into account in previous works and the performance of previous methods may be limited. To overcome the limitation, a method for sentiment-aware per- sonalized tweet recommendation through multimodal Field-aware Factorization Machines (FFM) is newly proposed in this paper. Successful personalized tweet recommendation becomes feasible through the following three contributions: (i) sentiment factors are newly introduced into personalized tweet recommendation, (ii) users’ interest is modeled by deriving multimodal FFM that enables collabora- tive use of multiple factors in a tweet, i.e., publisher, topic and sentiment factors, and (iii) the effectiveness of using sentiment factors as well as publisher and topic factors is clarified from results of experiments using real-world datasets related to worldwide hot topics, “#trump”, “#hillaryclinton” and “#ladygaga”. In addition to showing the effectiveness of the proposed method, the applicability of the pro- posed method to other tasks such as advertisement and social analysis is discussed as a conclusion and future work of this paper.