Referral Web: combining social networks and collaborative filtering
Communications of the ACM
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Recommending collaboration with social networks: a comparative evaluation
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
An automatic weighting scheme for collaborative filtering
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Using Singular Value Decomposition Approximation for Collaborative Filtering
CEC '05 Proceedings of the Seventh IEEE International Conference on E-Commerce Technology
Generating predictive movie recommendations from trust in social networks
iTrust'06 Proceedings of the 4th international conference on Trust Management
Patterns of influence in a recommendation network
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
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Commercial enterprises employ data mining techniques to recommend products to their customers. Most of the prior research is usually focused on a specific domain such as movies or books, and recommendation algorithms using similarities between users and/or similarities between products usually performs reasonably well. However, when the domain isn't as specific, recommendation becomes much more difficult, because the data could be too sparse to find similar users or similar products based on purchasing history alone. To solve this problem, we propose using social network data, along with rating history to enhance product recommendations. This paper exploits the state of art collaborative filtering algorithm and social net based recommendation algorithm for the task of open domain recommendation. We show that when a social network can be applied, it is a strong indicator of user preference for product recommendations. However, the high precision is achieved at the cost of recall. Although the sparseness of the data may suggest that the social network is not always applicable, we present a solution to utilize the network in these cases.