Link prediction approach to recommender systems

The problem of recommender system is very popular with myriad available solutions. Recommender systems recommend items to users and help them in narrowing their search from huge amount of options available to the user. In this work, a novel approach for the recommendation problem is proposed by incorporating techniques from the link prediction problem in social networks. The proposed approach models the typical user-item information as a bipartite network, and predicts future links using link prediction measures, 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 local neighborhoods in the network, this approach would lead to a scalable solution to recommendation. In this work, we present top k links that are predicted by link prediction measures as recommendations to the users. Our work initially applies different existing link prediction measures to the recommendation problem by making suitable adaptations. The prime contribution of this work is to propose a recommendation framework routed from link prediction problem in social networks, that effectively utilizes probabilistic measures of link prediction and embed temporal data accessible on existing links. The proposed approach is evaluated on one movie-rating dataset of MovieLens, two product-rating datasets of Epinions & Amazon and one hotel-rating dataset of TripAdvisor. Results show that the link prediction measures based on temporal probabilistic information prove to be more effective in improving the quality of recommendation. Especially, Temporal cooccurrence probability measure improves the area under ROC curve (AUROC) by 10% for MovieLens, 23% for Epinions, 17% for TripAdvisor, 9% for Amazon over standard item-based collaborative filtering method. Similar improved performance is observed in terms of area under Precision-Recall curve (AUPR) as well as Normalized Rank-Score.