Given the increasingly large amount of information on the Internet, recommendation systems have been widely used to recommend interesting information to the users so that they do not have to proactively look for it by using search engines or visiting websites. Recommendation systems can help users filter out irrelevant information, thus reducing the information overloading problem. Further, by monitoring users actions on the recommendations, information producers can gain a better understanding of the users’ interest and hence can focus their resources on delivering information that would arouse the users interest.

In parallel with the advent of recommendation systems, online social media has gained dramatic increase in usage across different communities. The social relation...[ Read more ]

Given the increasingly large amount of information on the Internet, recommendation systems have been widely used to recommend interesting information to the users so that they do not have to proactively look for it by using search engines or visiting websites. Recommendation systems can help users filter out irrelevant information, thus reducing the information overloading problem. Further, by monitoring users actions on the recommendations, information producers can gain a better understanding of the users’ interest and hence can focus their resources on delivering information that would arouse the users interest.

In parallel with the advent of recommendation systems, online social media has gained dramatic increase in usage across different communities. The social relations created in social networks are of great value in improving the performance of traditional recommendation systems because people who are socially related have strong influence on each other in their interests, tastes and opinions, etc. The synergy between recommendation systems and social networks has received much attention from researchers in both industry and academia.

In this thesis, we develop a new technique to integrate social information into recommendation systems to improve recommendation quality. We choose matrix factorization model as the basic recommendation framework upon which we add social information. In matrix factorization model, both users and items are mapped into a joint latent factor space and the user-item ratings are inner products of vectors in that space. To take advantage of social information, we introduce regularization to influence the result of matrix factorization. Previous methods typically use user-based regularization to make recommendations of socially connect users as close to each other as possible. Unfortunately, it is hard to identify users with similar interests because a user may have multiple interests and having some identical interests does not mean that the users have identical interests overall. Instead of using user-based regularization, our proposed method introduces item-based regularization. The advantage of our approach is that comparing to the diversity of user interest, an item typically has specific purposes, properties or applications, making it easier to identify similar items from the users who are interested in them. For example, a user is interested in a book on Data Mining and another user is interested in a book on Machine Learning, we can infer that they have some similar interests but cannot conclude that they have identical interests because they may have different interests in other domain (e.g., movie). On the other hand, there should be much overlap between users who are interested in a book on Data Mining and users who are interested in a book on Machine Leaning. Thus, the two books should be similar and share close latent vectors. We conduct experiments using a dataset from Douban, and Mean Average Error and Root Mean Square Error are used as performance metrics. We demonstrate that our method can improve the performance in the recommendation tasks.