Link Prediction Approach to Recommender Systems

The problem of recommender system is very popular with myriad available solutions. A novel approach that uses the link prediction problem in social networks has been proposed in the literature that model the typical user-item information as a bipartite network in which link prediction would actually mean recommending an item to a user. The standard recommender system methods suffer from the problems of sparsity and scalability. Since link prediction measures involve computations pertaining to small neighborhoods in the network, this approach would lead to a scalable solution to recommendation. One of the issues in this conversion is that link prediction problem is modelled as a binary classification task whereas the problem of recommender systems is solved as a regression task in which the rating of the link is to be predicted. We overcome this issue by predicting top k links as recommendations with high ratings without predicting the actual rating. Our work extends similar approaches in the literature by focusing on exploiting the probabilistic measures for link prediction. Moreover, in the proposed approach, prediction measures that utilize temporal information available on the links prove to be more effective in improving the accuracy of prediction. This approach is evaluated on the benchmark 'Movielens' dataset. We show that the usage of temporal probabilistic measures helps in improving the quality of recommendations. Temporal random-walk based measure T_Flow improves recommendation accuracy by 4% and Temporal cooccurrence probability measure improves prediction accuracy by 10% over item-based collaborative filtering method in terms of AUROC score.