2017 IEEE International Conference on Big Knowledge (ICBK) (2017)
Hefei, Anhui, China
Aug. 9, 2017 to Aug. 10, 2017
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICBK.2017.14
In this paper, we propose EIGENREC; a simple and versatile Latent Factor framework for Top-N Recommendations, which includes the well-known PureSVD algorithm as a special case. EIGENREC builds a low dimensional model of an inter-item proximity matrix that combines a traditional similarity component, with a scaling operator, designed to regulate the effects of the prior item popularity on the final recommendation list. A comprehensive set of experiments on the MovieLens and the Yahoo datasets, based on widely applied performance metrics suggest that EIGENREC outperforms several state of-the-art algorithms, in terms of Standard and Long-Tail recommendation accuracy, while exhibiting low susceptibility to the problems caused by Sparsity, even its most extreme manifestations – the Cold-start problems.
Symmetric matrices, Computational modeling, Buildings, Recommender systems, Matrix decomposition, Algorithm design and analysis
A. N. Nikolakopoulos, V. Kalantzis, E. Gallopoulos and J. D. Garofalakis, "Factored Proximity Models for Top-N Recommendations," 2017 IEEE International Conference on Big Knowledge (ICBK), Hefei, Anhui, China, 2017, pp. 80-87.