A Dual Hybrid Recommender System based on SCoR and the Random Forest

Costas Panagiotakis1, Harris Papadakis2 and Paraskevi Fragopoulou2

  1. Department of Management Science and Technology, Hellenic Mediterranean University
    72100 Agios Nikolaos, Crete, Greece, Tel.: +30-28410-91203
    cpanag@hmu.gr
  2. Department of Electrical and Computer Engineering, Hellenic Mediterranean University
    71004 Heraklion, Crete, Greece, Tel.: +30-2810-379119
    adanar@hmu.gr, fragopou@ics.forth.gr

Abstract

We propose a Dual Hybrid Recommender System based on SCoR, the Synthetic Coordinate Recommendation system, and the Random Forest method. By combining user ratings and user/item features, SCoR is initially employed to provide a recommendation which is fed into the Random Forest. The two systems are initially combined by splitting the training set into two “equivalent” parts, one of which is used to train SCoR while the other is used to train the Random Forest. This initial approach does not exhibit good performance due to reduced training. The resulted drawback is alleviated by the proposed dual training system which, using an innovative splitting method, exploits the entire training set for SCoR and the Random Forest, resulting to two recommender systems that are subsequently efficiently combined. Experimental results demonstrate the high performance of the proposed system on the Movielens datasets.

Key words

recommender systems, synthetic coordinates, random forest

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS200515046P

Publication information

Volume 18, Issue 1 (January 2021)
Year of Publication: 2021
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Panagiotakis, C., Papadakis, H., Fragopoulou, P.: A Dual Hybrid Recommender System based on SCoR and the Random Forest. Computer Science and Information Systems, Vol. 18, No. 1, 115–128. (2021), https://doi.org/10.2298/CSIS200515046P