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.
['Sarah K. Tyler', 'Yi Zhang']
Open Domain Recommendation: Social Networks and Collaborative Filtering