The Book Recommendation System project is all about creating an engaging and personalized experience for users as they navigate through a digital library. By harnessing the power of a Nearest Neighbors model, we've designed a system that offers book recommendations tailored to individual preferences. This interactive platform empowers users to select a book they're interested in, and in return, it dynamically generates suggestions based on similar user tastes and preferences.
Based on the provided context and the file "Book_Recommendation_System.ipynb," here is a formatted README file with a table of contents, short insightful descriptions, and sections covering feature engineering, preprocessing, analysis, and a recommendation system based on item-based collaborative filtering.
- Introduction
- Dataset Overview
- Feature Engineering
- Data Preprocessing
- Exploratory Data Analysis (EDA)
- Recommendation System
- Conclusion
This project aims to build a book recommendation system using collaborative filtering techniques. The focus is on understanding user preferences and recommending books based on these insights.
The dataset contains information about books, users, and their ratings. Key attributes include book titles, user IDs, and rating scores. This dataset is essential for training and evaluating the recommendation system.
Feature engineering involves creating new features from the existing data to improve the performance of the recommendation model. In this project, the following features were engineered:
- User Rating Count: The total number of ratings given by each user.
- Book Rating Count: The total number of ratings received by each book.
- Average Book Rating: The average rating of each book, providing insight into overall book popularity.
Data preprocessing involves cleaning and preparing the data for analysis. Key steps include:
- Handling Missing Values: Removing or imputing missing data to ensure completeness.
- Data Normalization: Normalizing rating scores to a common scale.
- Data Splitting: Dividing the data into training and test sets for model evaluation.
EDA is conducted to understand the data distribution and identify patterns. Key analyses include:
- Rating Distribution: Visualizing the distribution of ratings to understand user behavior.
- Top-Rated Books: Identifying the most popular books based on average ratings.
- Active Users: Analyzing the activity levels of users based on the number of ratings given.
The recommendation system uses collaborative filtering techniques to suggest books to users.
Collaborative filtering leverages user-item interactions to make recommendations. It assumes that users who agreed in the past will agree in the future.
Item-based collaborative filtering recommends books based on the similarity between items. It calculates the similarity between books using rating patterns and suggests books that are similar to those the user has rated highly.
- Similarity Calculation: Using cosine similarity to measure the similarity between books.
- Recommendation Generation: Suggesting top N books based on similarity scores.
This project demonstrates the implementation of a book recommendation system using item-based collaborative filtering. The system effectively recommends books by leveraging patterns in user ratings and item similarities, providing personalized recommendations to users.
For detailed code implementation and further insights, please refer to the accompanying Jupyter Notebook file Book_Recommendation_System.ipynb.