CROSS DOMAIN COLLABORATIVE FILTERING RECOMMENDER USING PROBABILISTIC MATRIX FACTORIZATION

M. Venu Gopalachari, Nazima Khanam

Abstract


Recommender systems (RS) are targeted towards users who lack sufficient experience to evaluate the overwhelming number of alternative examples that a system may offer. Collaborative Filtering RS is one of the approach that provide recommendations without taking into account the contents of the items being recommended however, they face several challenges such as cold start, data sparsity, low confidence etc. Of late there has been considerable interest in Cross Domain RS, where we exploit knowledge from auxiliary domains which contains additional user preference data to improve recommendation on target domains. This paper, focus on Probabilistic Matrix Factorization (PMF) model in Cross Domain Recommender (CDR) that outperforms on other model based Collaborative Filtering recommenders. Experiments are conducted on benchmark datasets shown significant improvement in the quality of the CDR over various test sets.

Keywords


cross domain; matrix factorization; collaborative filtering; transfer learning; recommender system

Full Text:

PDF


DOI: https://doi.org/10.26483/ijarcs.v8i9.4897

Refbacks

  • There are currently no refbacks.




Copyright (c) 2017 International Journal of Advanced Research in Computer Science