Open Domain Recommendation:Social networks and collaborative filtering
Kevin Tyler · Jisuanji kexue yu tansuo · 2009
Commercial enterprises employ data mining techniques to recommend products to their potential customers.Most of the prior research in recommender systems 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 perform 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,it proposes using social network data,along with rating history to enhance product recommendations.The state of art collaborative filtering algorithm and social net based recommendation algorithm are exploited for the task of open domain recommendation.It shows 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,a solution to utilize the network in these cases is presented.