EFFECTS OF THE HIGH-ORDER CORRELATION ON INFORMATION FILTERING
Lei Liu, Jian-Guo Liu, JING NI, RUI LENG, Kerui Shi, Qiang Guo, Xiaoming Xu · International Journal of Modern Physics C · 2012
In this paper, we empirically investigate the statistical properties of the user correlation network in terms of their common rated objects on MovieLens, and find that it has high clustering coefficient and ultra small average distance, which is close to the fully connected network. We argue that the above characteristics come from the fact that large-degree objects build lots of fully connected subnetworks by using the node projection method. By introducing the user global similarity, measured by the product of two users' similarity vectors, we present an effective way to identify users' specific interests by weakening the mainstream interests and noise interests. Numerical results show that we are able to obtain accurate and diverse recommendations by considering the second-order correlation redundant information simultaneously, which outperforms the state-of-the-art collaborative filtering (CF) methods. This work suggests that statistical properties of the user correlation network is an important factor to improve the performances of information filtering algorithms.