Pairwise learning to recommend with both users' and items' contextual information

Exponential growth of information generated by social networks requires efficient and scalable recommendation techniques to produce useful results. Traditional methods have become unqualified because they consider only ratings instead of rankings in an item list, and they ignore social contextual information, which is valuable for predicting users’ preference. It is significant and challenging to fuse social contextual information into learning to recommendation methods. In this study, the authors first extend user latent features by exploiting users’ social relationship such as friendship or trust relations, and extend item latent features with concurrent items. Then they integrate both users’ and items’ social contextual information into a pairwise learning to recommendation model (named as UIContextRank) to enhance ranking accuracy and recommendation quality. Furthermore, they extend UIContextRank in a distributed environment to improve efficiency and scalability. The authors conduct experiments on both bidirectional and unidirectional social network datasets. The results show that their method significantly outperforms other approaches.