Factored Proximity Models for Top-N Recommendations

A. N. Nikolakopoulos, Vasileios Kalantzis, E. Gallopoulos, J. D. Garofalakis

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    1 Scopus citations


    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.
    Original languageEnglish (US)
    Title of host publication2017 IEEE International Conference on Big Knowledge (ICBK)
    Number of pages8
    StatePublished - Aug 1 2017



    • Algorithm design and analysis
    • Buildings
    • Computational modeling
    • Matrix decomposition
    • Recommender systems
    • Symmetric matrices
    • Collaborative Filtering
    • Latent Factor Methods
    • PureSVD
    • Top-N Recommendation

    Cite this

    Nikolakopoulos, A. N., Kalantzis, V., Gallopoulos, E., & Garofalakis, J. D. (2017). Factored Proximity Models for Top-N Recommendations. In 2017 IEEE International Conference on Big Knowledge (ICBK) (pp. 80-87). IEEE. https://doi.org/10.1109/ICBK.2017.14