CoCo (Context vs. Content): Behavior-Inspired Social Media Recommendation for Mobile Apps. Yang, B., Wu, C., Sigg, S., & Zhang, Y. In 2016 IEEE Global Communications Conference (Globecom), December, 2016.
abstract   bibtex   
Exponential growth of media generated in online social networks demands effective recommendation to improve the efficiency of media access especially for mobile users. In particular, content, objective quality or general popularity are less decisive for the prediction of user-click behavior than friendship-conditioned patterns. Existing recommender systems however, rarely consider user behavior in real-life. By means of a large-scale data-driven analysis over real-life mobile Twitter traces from 15,144 users over a period of one year, we reveal the importance of social closeness related behavior features. This paper proposes CoCo, the first-ever behavior-inspired mobile social media recommender system to improve the media access experience. CoCo exploits representative behavior features via a latent bias based machine learning approach. Our comprehensive evaluation through trace-driven emulations on the Android app exposes a superior accuracy of 72.3%, with a small additional daily energy consumption of 1.3% and a monthly data overhead of 9.1MB.
@InProceedings{Wu_2016_globecom,
author={Bowen Yang and Chao Wu and Stephan Sigg and Yaoxue Zhang},
title={CoCo (Context vs. Content): Behavior-Inspired Social Media Recommendation for Mobile Apps},
booktitle={2016 IEEE Global Communications Conference (Globecom)},
year={2016},
month={December},
abstract={Exponential growth of media generated in online social networks demands effective recommendation to improve the efficiency of media access especially for mobile users. In particular, content, objective quality or general popularity are less decisive for the prediction of user-click behavior than friendship-conditioned patterns. Existing recommender systems however, rarely consider user behavior in real-life. By means of a large-scale data-driven analysis over real-life mobile Twitter traces from 15,144 users over a period of one year, we reveal the importance of social closeness related behavior features. This paper proposes CoCo, the first-ever behavior-inspired mobile social media recommender system to improve the media access experience. CoCo exploits representative behavior features via a latent bias based machine learning approach. Our comprehensive evaluation through trace-driven emulations on the Android app exposes a superior accuracy of 72.3%, with a small additional daily energy consumption of 1.3% and a monthly data overhead of 9.1MB.},
group = {ambience}}
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