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Mobile Contextual Recommender System for Online Social Media
2017
IEEE Transactions on Mobile Computing
Exponential growth of media consumption in online social networks demands effective recommendation to improve the quality of experience especially for on-the-go mobile users. By means of large-scale trace-driven measurements over mobile Twitter traces from users, we reveal the significance of affective features in shaping users' social media behaviors. Existing recommender systems however, rarely support such psychological effect in real-life. To capture such effect, in this paper we propose
doi:10.1109/tmc.2017.2694830
fatcat:dbw3zyqbdja7led4fazfaxzc4y