Robust product recommendation system using modified grey wolf optimizer and quantum inspired possibilistic fuzzy C-means
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Robust product recommendation system using modified grey wolf optimizer and quantum inspired possibilistic fuzzy C-means Likhesh Kolhe1 • Ashok Kumar Jetawat1 • Vaishali Khairnar2 Received: 30 May 2019 / Revised: 10 August 2020 / Accepted: 15 August 2020 Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract In recent years, several researchers have developed web-based product recommendation systems to assist customers in product search and selection during online shopping. In addition, the product recommendation systems deliver true personalization by recommending the products based on the other customer’s preferences. This study has investigated how the product recommendation system influences the customer’s decision effort and quality. In this study, the proposed system comprises of five major phases: data collection, pre-processing, key word extraction, keyword optimization and similar data clustering. The input data were collected from amazon customer review dataset. After the data collection, preprocessing was carried-out to enhance the quality of collected amazon data. The pre-processing phase comprises of two systems lemmatization and removal of stop-words & uniform resource locators (URLs). Then, a superior topic modelling method Latent Dirichlet allocation (LDA) along with modified grey wolf optimizer (MGWO) was applied in order to identify the optimal keywords. The extracted key-words were clustered into two forms (positive and negative) by applying a clustering algorithm named as quantum inspired possibilistic fuzzy C-means (QIPFCM). Experimental results showed that the proposed system achieved better performance in the product recommendation system compared to the existing systems in terms of accuracy, precision, recall and f-measure. Keywords Grey wolf optimizer Latent Dirichlet allocation Lemmatization Possibilistic fuzzy C-means Recommendation system
1 Introduction In recent years, the rapid growth of social networking changes the global business and daily lives of human beings. Presently, most of the people tend to purchase the products online, because it is quiet convenient [1, 2]. In spite of success of e-commerce (online shopping), it faces many difficulties. In off-line stores, staffs are available for & Likhesh Kolhe [email protected]; [email protected] Ashok Kumar Jetawat [email protected] Vaishali Khairnar [email protected] 1
Department of Computer Engineering, Pacific Academy of Higher Education & Research University, Udaipur, India
2
Department of Information Technology, Terna Engineering College, Nerul, India
assisting the customers, but there are no staffs are available for assisting the buyers in online stores [3]. To address this issue, online stores offer several features like recommendation system for assisting the customers. The recommendation system is an innovative solution, which helps to overcome the concerns of e-commerce services. Usually, recommendation system uses custome
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