Web Scraping based Product Comparison Model for E-Commerce Websites

Harsh Khatter, Dravid, Akshat Sharma, Ajay Kumar Kushwaha

2022 IEEE International Conference on Data Science and Information System (ICDSIS)(2022)

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Abstract
Existing e-commerce applications offer various functionalities to buy any products from their websites. However, a comparison for any product in terms of price offers, and quality among these applications is time-consuming and involves the user’s time to check reviews and surf other websites to check prices. The objective of this paper is to propose a web application that identifies those basic details for any product from different e-commerce websites. These details are compared, and the result is displayed to the user graphically for the final decision. The proposed web application uses the web-scraping methodology with selenium and is implemented on the python framework using various algorithms and techniques which are discussed in the paper. As an outcome, the system will give the simulation results, so that the user can get the recommendations on purchasing the relevant product with better user satisfaction and in minimum clicks and time.
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Key words
E-Commerce,Product Recommendation,Python Programming,Recommendation,Selenium,Web Scraping.
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