Publication Type:

Journal Article

Source:

Advances in Intelligent Systems and Computing, Springer Verlag, Volume 508, p.561-570 (2017)

ISBN:

9789811027499

URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85018690651&doi=10.1007%2f978-981-10-2750-5_58&partnerID=40&md5=a45688eb4a04d31711ddd835b3d1612c

Keywords:

Cold start, Cold-item, Cold-user, Heat spread, Information filtering, Iterative methods, Local least squares, Recommender systems, Sparsity

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.

Notes:

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

Cite this Research Publication

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.

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