An efficient semantic recommender method forArabic text

Bilal Hawashin (Al-Zaytoonah University of Jordan, Amman, Jordan)
Shadi Alzubi (Al-Zaytoonah University of Jordan, Amman, Jordan)
Tarek Kanan (Al-Zaytoonah University of Jordan, Amman, Jordan)
Ayman Mansour (Tafila Technical University, Tafila, Jordan)

The Electronic Library

ISSN: 0264-0473

Publication date: 1 April 2019

Abstract

Purpose

This paper aims to propose a new efficient semantic recommender method for Arabic content.

Design/methodology/approach

Three semantic similarities were proposed to be integrated with the recommender system to improve its ability to recommend based on the semantic aspect. The proposed similarities are CHI-based semantic similarity, singular value decomposition (SVD)-based semantic similarity and Arabic WordNet-based semantic similarity. These similarities were compared with the existing similarities used by recommender systems from the literature.

Findings

Experiments show that the proposed semantic method using CHI-based similarity and using SVD-based similarity are more efficient than the existing methods on Arabic text in term of accuracy and execution time.

Originality/value

Although many previous works proposed recommender system methods for English text, very few works concentrated on Arabic Text. The field of Arabic Recommender Systems is largely understudied in the literature. Aside from this, there is a vital need to consider the semantic relationships behind user preferences to improve the accuracy of the recommendations. The contributions of this work are the following. First, as many recommender methods were proposed for English text and have never been tested on Arabic text, this work compares the performance of these widely used methods on Arabic text. Second, it proposes a novel semantic recommender method for Arabic text. As this method uses semantic similarity, three novel base semantic similarities were proposed and evaluated. Third, this work would direct the attention to more studies in this understudied topic in the literature.

Keywords

Citation

Bilal Hawashin, Shadi Alzubi, Tarek Kanan and Ayman Mansour (2019) "An efficient semantic recommender method forArabic text", The Electronic Library, Vol. 37 No. 2, pp. 263-280

Download as .RIS

DOI

: https://doi.org/10.1108/EL-12-2018-0245

Publisher

:

Emerald Publishing Limited

Copyright © 2019, Emerald Publishing Limited

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