Effect of Different Implicit Social Networks on Recommending Research Papers

Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. However, all approaches in the domain of research paper recommendation have used explicit social relations that users have initiated which has the problem of low recommendation coverage. We argued that the available data in social bookmarking Web sites such as CiteULike or Mendeley could be exploited to connect similar users using implicit social connections based on their bookmarking behavior. In this paper, we proposed three different implicit social networks-readership, co-readership, and tag-based and we compared the recommendation accuracy of several recommendation algorithms using data from the proposed social networks as input to the recommendation algorithms. Then, we tested which implicit social network provides the best recommendation accuracy. We found that, for the most part, the social recommender is the best algorithm and that the readership network with reciprocal social relations provides the best information source for recommendations but with low coverage. However, the co-readership network provide good recommendation accuracy and better user coverage of recommendation.