Title: IoT enabled machine learning framework for social media content-based recommendation system

Authors: Adinarayana Salina; E. Ilavarasan; Yogeswara Rao Kalla

Addresses: Department of CSE, Raghu Institute of Technology, Visakhapatnam, Andhra Pradesh, India ' Department of CSE, Pondicherry Engineering College, Pondicherry, India ' Department of CSE, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, India

Abstract: Analysing huge volume of data from the social media tweets on product reviews provides a better understanding of any product. Exploring customer opinions from tweets is helpful to find the strengths and weaknesses of different products and features. There are several studies on product recommendations from Twitter product reviews. In this paper, Internet of Things-based two level product recommendation framework (TLPRF) is proposed to efficiently handle large amount of Twitter users' product reviews data. TLPRF consists of a Raspberry Pi microcomputer as an IoT mining machine and it is programmed to generate a feature level opinion summary. Feature level opinion is found to be useful in accomplishing the product ranking. Based on the customer interest in the product purchase request, a normalised ranking of each matching product is calculated from the feature-wise opinion summary and the product with maximum ranked score is recommended to Twitter user. The proposed TLPRF is found to be superior to similar other approaches in terms of accuracy, precision, recall and f-measure.

Keywords: internet of things; machine learning; ranking; summarisation; social networks; recommendation systems.

DOI: 10.1504/IJVICS.2022.122555

International Journal of Vehicle Information and Communication Systems, 2022 Vol.7 No.2, pp.161 - 175

Received: 28 Jul 2020
Accepted: 07 Apr 2021
Published online: 03 May 2022 *

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