Social recommendation using quantified social tie strength

With the development of online social network (OSN), social recommendation approaches have gain more and more momentum. The users´ OSN interactions which reflect the social tie strength put forward social recommendation approaches. But most of the previous work just classify the social tie strength into strong one and weak one. The coarse-grained social tie strength can not accurately reflect the social relationships between users and naturally affect the recommendation results. To address this problem, this paper presents a recommendation approach based on quantified social tie strength. We propose an unsupervised method to estimate tie strength from user similarity and online social interactions. Then the approach improve the social recommendation with quantified social tie strength. Experiments are made on a large book rating dataset from Douban.com. The experimental results show that this approach can effectively improve the recommendation accuracy.