Inferring Social Networks Based on Movie Rating Data

Social network analysis has been a hot research area recently thanks to the availability of many large dataset. One of the problems in social network analysis is to recover the underlying network structure given information about how nodes in the network interact with each other in the past. In the context of social movie website (e.g. Flixster1), if we know when users rate movies, can we find an optimal network of users that best explain the propagation of information which influences their movie watching behavior? Unveiling such a network would be useful to identify how product information is spread among users. Moreover, it is useful for recommendation of new products to users since recent studies have pointed out that using social network information could potentially improve the accuracy of recommendation. But many movie website such as Netflix2 do not have built-in social network to take this advantage. Our contributions are three-folds: (1) We analyze the temporal movie rating data from Flixster; (2) We propose a social network inference algorithm MOVINF, which is more accurate and efficient; (3) We evaluate the inferred network using recommendation (‘Netflix Challenge’), which outperforms baseline by 1.41% and is almost as good as using original social network.