Mobile Contextual Recommender System for Online Social Media

Abstract

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 Kaleido, a real mobile system that achieves an online social media recommendation solution by taking affective context into account. Specifically, we design a machine learning mechanism to infer the affective pulse of online social media. Furthermore, a cluster-based latent bias model (LBM) is provided for jointly training the affective pulse as well as user's behavior, location, and social contexts. Our comprehensive trace-driven experiments on Android prototype expose a superior prediction accuracy of 87 percent, which has 25 percent accuracy superior to 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.