The emergence of the online media sharing sites (e.g. Youtube, Youku, and Hulu) have introduced new challenges in program recommendation in online networks. However, there is a bottleneck that the amount of available viewing logs and user friendship networks are too limited to design effective recommendation algorithms. Thus, carrying out an intelligent program recommendation system is important for these sites. In this work, we propose a novel model which turns to the social networks and mine user preferences information expressed in microblogs for evaluating the similarity between online movies and TV episodes. To the best of our knowledge, it is the first effort to bridge the gap between movie and TV watchers domain with social network activities. Moreover, it is the first approach that can solve the "cold-start" problem in movie and TV recommendation system. Series of data mining approaches and social computing models have been adopted in this work. Similar programs found from the social network are further used to suggest programs in other media devices. This work can be easily applied in online media streaming sites in order that intelligent recommendations of programs can be made to the customers through mining microblogs.
['Hui Li', 'Jiangtao Cui', 'Bingqing Shen', 'Jianfeng Ma']
An intelligent movie recommendation system through group-level sentiment analysis in microblogs