A Recommendation Method Based on Semantic Similarity and Complementarity Using Weighted Taxonomy: A Case on Construction Materials Dataset

Karamollah Bagherifard, Mohsen Rahmani (), Vahid Rafe () and Mehrbakhsh Nilashi
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Karamollah Bagherifard: Department of Computer Engineering, Faculty of Engineering, Arak University, Arak 38156-8-8349, Iran†Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran
Mohsen Rahmani: Department of Computer Engineering, Faculty of Engineering, Arak University, Arak 38156-8-8349, Iran
Vahid Rafe: Department of Computer Engineering, Faculty of Engineering, Arak University, Arak 38156-8-8349, Iran
Mehrbakhsh Nilashi: Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia4Department of Computer Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran

Journal of Information & Knowledge Management (JIKM), 2018, vol. 17, issue 01, 1-26

Abstract: Products and web pages are the main components of the e-commerce data knowledge and the relationship among them is an important issue to be highly considered in recommender systems. This study aims to focus on the similarity and complementarity relationships among the products that have wide applications in the recommender systems. In the previously proposed methods, products and their relationships were revealed using taxonomy and “IS-A” relationship. In addition, the similarity and complementarity calculations were conducted based on edge computation by assigning a similar degree to any edge. More specifically, the children of a concept in the taxonomy was supported by a similar father’s “IS-A” degree. In contrast, this study provides a new approach based on ontology, data mining, and automatic discovering algorithms for the relationships with different degrees for the edges among the concepts. Accordingly, these relationships are initialised according to the “IS-A” degree. With regard to this weighted taxonomy, the semantic similarity and complementarity are measured based on concept distance. In addition, the proposed recommender system is item-based, which uses semantic similarity and complementarity. The required data for the present study were collected from construction materials supplier. The results illustrated that our proposed method is effective for construction materials recommendation.

Keywords: Recommender system; ontology; semantic similarity; semantic complementarity (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1142/S0219649218500107

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