A location-aware TV show recommendation with localized sementaic analysis

Nowadays, microblogging platforms such as Twitter and Weibo can also be seen as a good media to present reviews about topics. More and more people tend to share their thoughts through various microblogging sites. For example, when a TV show is being shown, the users would like to share and discuss their opinions about it on these platforms. However, one phenomenon is the popularity of the TV usually varies for different regions due to the cultural differences, custom and some other factors. Predicting whether a TV show will be popular at certain locations is then desirable. In this paper, a location-aware TV show recommendation scheme is proposed. By incorporating the social network information of users from different locations, a location-based user profile is obtained. Then, the scheme conducts prediction of TV show popularity for different regions based on the profile and similar shows. For a new TV show, the popularity of the similar shows is utilized to get the initial location-show matrix. Then, the location profiles and physical distance are used as regularizer into the collaborative filtering framework to further refine the prediction. A TV show dataset with location-aware social network information has been collected. Experiments have been conducted on real data and encouraging results have been achieved.