Exploiting opinion distribution for topic recommendation in Twitter

The popular social networking service Twitter provides rapidly updated information and online trends, which enriches and benefits people's daily life. At the same time, how to find out the really interesting and relevant topics from the massive streams of tweets, to provide precise topic recommendation for users, becomes a challenging problem in the real world. Previous collaborative filtering methods give solutions to traditional recommendation tasks considering users' positive reviews to help recommend items. However, to the problem what is interesting to whom' in Twitter, positive opinions toward a topic do not imply that the user will be interested in it with high probability, for the user probably prefers to know those controversial topics or hot events with a large number of negative posts. In this paper, we exploit the characteristics of topical opinion distribution for improving the performance of recommendation. The experimental results on a real-world Twitter dataset show that the proposed opinion-distribution-aware topic recommendation (ODA-TR) approach outperforms the state-of-the-art collaborative recommendation methods. (c) 2015 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.