A Hybrid Recommendation System in Co-authorship Networks

Recommender systems are an important field that aim to direct users more quickly to their needs. Scientific research studies in academic field are mostly carried out by collaboration between scientists. Finding new collaborations and analyzing the quality of them are quite complicated situations. Therefore, developing a system that recommend strong collaborations for scientists can be an invaluable tool. In this study, we present a hybrid recommendation method that uses both collaborative filtering and a network-based method by examining the structure of the co-authorship network that was created by using a real database of scientific articles in computer science. In the constructed network, the nodes represent scientists and links represent the coauthorship relationships. If two scientists in the network have been co-author of at least one article, they are considered to be connected. Many of the present recommendation methods in this area take into account only the common neighbors of the nodes, while calculating the similarity of the node pairs in the network. To overcome these limited methods, we use the local community-based similarity indexes which also take into account the similarities between the common neighbors of the nodes. When the experimental results are examined, they prove the success of the proposed method.