Building a Book Recommendation System

Business Scenario

The project is designed to recommend books to users based on their preferences. It uses data scraped from free scraping website Books to Scrape and provides personalized recommendations through a web-based interface. This project integrates data scraping, preprocessing, machine learning, and a user-friendly interface to deliver a complete book recommendation solution, which could be used in e-commerce platforms, libraries, or book-related applications to enhance user experience and drive engagement.

Techniques

Web Scraping: Libraries like beautifulsoup4, selenium, and scrapy are used to scrape book data (e.g., titles, prices, ratings, descriptions) from websites.
Data Preprocessing: pandas is used for data cleaning and transformation (e.g., cleansing prices, extracting stock numbers, mapping ratings). Regular expressions (re) are used to clean text and extract specific patterns.
Recommendation Engine: scikit-learn is used to implement a content-based recommendation system using TF-IDF vectorization and cosine similarity to find books similar to a user’s query.
Interactive User Interface: streamlit library is used to create a web-based UI for users to interact with the system, input queries, and view recommendations.

Workflow

1. Data Collection

Scrape book data using one of three methods: beautifulsoup4, selenium, or scrapy.

2. Data Preprocessing

The scraped data is cleaned and transformed (e.g., removing special characters, converting prices, mapping ratings).

3. Building Recommendation System

The system uses TF-IDF vectorization to analyze book titles or descriptions and computes cosine similarity to recommend books similar to the user’s query.

4. Model Deploying

A web-based interface built with streamlit allow users to interact with the system to get personalized book recommendations based on text input or filter data.