  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.
