Title: OntoCommerce: an ontology focused semantic framework for personalised product recommendation for user targeted e-commerce
Authors: Gerard Deepak; Dheera Kasaraneni
Addresses: Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore-560001, India ' Department of Computer Science and Engineering, University Visvesvaraya College of Engineering, Bangalore University, Bangalore-560001, India
Abstract: With the paradigm shift in business strategy in terms of online marketing and e-commerce and to comprehend the World Wide Web transforming into an intelligent semantic web, there arises a perpetual need for semantically driven e-commerce systems which gives preference to the users. In this paper, OntoCommerce which is an e-commerce system that incorporates semantic algorithms for product recommendation has been proposed. The proposed strategy uses the enriched normalised pointwise mutual information measure for semantic similarity computation. OntoCommerce assimilates ontologies and recommends products based on the user query, recorded user navigation as well as the user profile analysis thereby encompassing personalisation. In order to make the recommendations more relevant, OntoCommerce uses parametric fuzzification to increase the number of relevant recommendable products. OntoCommerce yields an average accuracy of 88.68 % with a low false discovery rate of 0.13 which makes it a best-in-class semantically driven product recommendation system for online e-commerce.
Keywords: e-commerce; ontologies; personalised recommendation system; semantic similarity; enriched normalised pointwise mutual information; EnNPMI.
International Journal of Computer Aided Engineering and Technology, 2019 Vol.11 No.4/5, pp.449 - 466
Received: 12 Nov 2016
Accepted: 10 Feb 2017
Published online: 29 Mar 2019 *