Collaborative Ranking Friend Recommendation Algorithm in Heterogeneous Social Network
Kai Chen · Journal of Chinese Computer Systems · 2014
The social network is more and more complex and heterogeneous,traditional friend recommendation algorithm cannot cope the new situation for its ineffectiveness. This paper proposes a collaborative ranking based friend recommendation method by expending the traditional factor model. Comparing to the collaborative filtering friend recommendation methods,we replace the original scoring method with the partial order relation between the users to satisfy the needs of heterogeneous social network which is suitable for some situations that are hard to convert rates and it's of no need to precisely calculate the rates of low users in Top-k recommendation so that it is helpful to enhance the efficiency. According to the experiment results,our method is easy for friend recommendation to build training data and gets better results in learning user's interest than factorization model. The method can also mixes valuable content related feature easily. After testing in dataset of Digg2009,Collaborative ranking get 15. 6% higher than Matrix factorization in MAP.