Recommender Systems are a subclass of information filtering systems that seek to predict the preferences of a user or the preference that a user would give to an item. The most common problem faced by these systems is the lack of data. Such a situation leads to a matrix that is extremely sparse thus reducing the accuracy of prediction. Cold-start problem is one such problem that is faced by the recommender systems when a new user or a new item enters the system. We are hoping to reduce the cold-user and the cold-item problem by reducing the sparsity of the sparse matrix with the help of Iterative Local Least Squares algorithm and a hybrid of Heat Spreading algorithm and Probability Spreading algorithm. © Springer Nature Singapore Pte Ltd. 2017.
cited By 0; Conference of International Conference on Communication on Networks, COMNET 2016 ; Conference Date: 19 February 2016 Through 20 February 2016; Conference Code:190769
K. R. Bindu, Visweswaran, R. L., Sachin, P. C., Solai, K. D., and Gunasekaran, S., “Reducing the cold-user and cold-item problem in recommender system by reducing the sparsity of the sparse matrix and addressing the diversity-accuracy problem”, Advances in Intelligent Systems and Computing, vol. 508, pp. 561-570, 2017.