Product Recommendation System based on User Trustworthiness &amp; Sentiment Analysis

Gunjeet Kaur Soor,Deepali Vora,Rohit Dalal,Amey Morje,M.D Patil,V.A Vyawahare

doi:10.1051/itmconf/20203203030

Abstract

The current online product recommendation system based on reviews has many limitations due to randomness in the review patterns. The data which is used are the reviews and ratings from the e-commerce websites. This data might contain fake reviews that make the data uncertain. Due to this, the currently existing systems produce ambiguous results on this present data. Instead of this, the new system uses only genuine reviews, considering the trustworthiness of the user and generates the results in a more significant manner. The proposed system scrapes reviews from different online websites and performs opinion mining and sentiment analysis on it. Other factors like star ratings, the buyer’s profile and previous purchases and whether the review has been given after purchasing or not are included. Based on these factors & user trustworthiness, the website from which the user should buy the product will be recommended.

Highlights

A product recommendation system predicts and shows the items that a user would like to purchase
The idea was generated on the paper which was published in 2017 in IEEE Transactions on Information Forensics and Security which was based on spam detection and fake social media reviews [1]
The proposed design has been implemented block-wise as scrapers for different websites, the trustworthiness model and the sentiment analysis model and in addition, an user interface is developed

Summary

Introduction

A product recommendation system predicts and shows the items that a user would like to purchase. The results might not be entirely accurate. It shows results according to your likes, and recent searches. Recommender Systems have become more useful in recent years. They are utilized in various fields which include movies, music, news, books, research articles, search queries, social tags, and products in general. Make use of their proprietary recommendation algorithms in order to better serve the customers with the products they are bound to like [2] Used in the digital domain, the majority of today’s E-Commerce sites like eBay, Amazon, Flipkart, Alibaba, etc. make use of their proprietary recommendation algorithms in order to better serve the customers with the products they are bound to like [2]

Methods

Results

Conclusion