Web Scraping For Book Recommendation System

Abstract

The purpose of a book recommendation system is to predict buyer's interest and recommend books to them accordingly. Personal recommendation systems have been emerged to conduct effective search which mine related books based on user rating and interest. This paper proposed an effective system for recommending books for online users by providing the data which not only counts the ratings but also the users vote for the best books of 2022 along with their genre by using web scraping. Web scraping, also known as web extraction or harvesting, is a technique to extract data from the World Wide Web (WWW) and save it to a file system or database for later retrieval or analysis. Rather than using big data, smart data would work much better. The proposed system used Beautiful Soup designed and selenium web drivers for scraping HTML documents. Convenient Pythonic functions for navigating, searching, and modifying a parse tree; a toolkit for decomposing an HTML file and extracting desired information via html parser. The required data was successfully scraped or extracted and saved in csv file. Further a book recommendation model needs to be build using this dataset.