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Syafrurrizal Naridho
University of Trunojoyo Madura
Agriculture, Biological Sciences & Forestry Computer Science & IT Electrical & Electronics Engineering Engineering Physics

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Multi-criteria based Item Recommendation Methods Noor Ifada; Syafrurrizal Naridho; Mochammad Kautsar Sophan
Rekayasa Vol 12, No 2: Oktober 2019
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (420.836 KB) | DOI: 10.21107/rekayasa.v12i2.5913

Abstract

This paper comprehensively investigates and compares the performance of various multi-criteria based item recommendation methods. The development of the methods consists of three main phases: predicting rating per criterion; aggregating rating prediction of all criteria; and generating the top-  item recommendations. The multi-criteria based item recommendation methods are varied and labelled based on what approach is implemented to predict the rating per criterion, i.e., Collaborative Filtering (CF), Content-based (CB), and Hybrid. For the experiments, we generate two variations of datasets to represent the normal and cold-start conditions on the multi-criteria item recommendation system. The empirical analysis suggests that Hybrid and CF are best implemented on the normal and cold-start item conditions, respectively. On the other hand, CB should never be (solely) implemented in a multi-criteria based item recommendation system on any conditions.
Co-Authors Mochammad Kautsar Sophan Noor Ifada