Bindu K. R. currently serves as Assistant Professor in the Department of Computer Science and Engineering, School of Engineering, Coimbatore Campus. She is pursuing her research in Information retrieval. Her areas of interest include Information Retrieval, Grid Computing and Graph databases.


Publication Type: Journal Article

Year of Publication Title


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.[Abstract]

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. More »»