Fast Computing Method for Items Recommendation Based on Shortest-Path Trust Relationship

In order to increase the speed of collaborative filtering recommendation in social networks, an improved nearest-neighbor algorithm is proposed in this paper. The proof of its correctness is also given in detail. The similarity measurement between users is based on trust relationship by using shortest path method. Layered graph and dynamic programming are applied to calculate the similarity. Furthermore, the recommendation speed can also be improved by limiting the depth of relationship chain in practical applications of social networks. The Comparative simulations are carried out based on the KDD Cup 2012 Track datasets. The results show that the better balance between the accuracy and the recommendation efficiency can be achieved by the proposed algorithm.