Affective Contextual Mobile Recommender System

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 this psychological effect in real-life. To capture this effect, in this paper we propose Kaleido, a real mobile system to achieve an affect-aware learning-based social media recommendation.Specifically, we design a machine learning mechanism to infer the affective feature within media contents. Furthermore, a cluster-based latent bias model is provided for jointly training the affect, behavior and social contexts. Our comprehensive experiments on Android prototype expose a superior prediction accuracy of 82%, with more than 20% accuracy improvement over existing mobile recommender systems. Moreover, by enabling users to offload their machine learning procedures to the deployed edge-cloud testbed, our system achieves speed-up of a factor of 1,000 against the local data training execution on smartphones.