This repository presents a sophisticated recommender system developed for Goodreads, aimed at refining the user experience through highly personalized book recommendations. Our project introduces two innovative content-based recommendation approaches, compared against traditional machine learning techniques such as collaborative filtering, matrix factorization, neural collaborative filtering, and graph-based models. Each method's performance is rigorously analyzed and benchmarked to evaluate its effectiveness in enhancing recommendation accuracy and user satisfaction.
Goodreads is a renowned social cataloging website where users can explore books, write reviews, and manage their reading lists. Our recommender system aims to leverage the intrinsic and collaborative data from millions of user interactions to suggest books that align with individual preferences.
The Goodbooks-10k dataset encompasses six million ratings for ten thousand of the most popular books, detailed with extensive metadata. This dataset provides a robust foundation for building and evaluating recommender systems. Here is a breakdown of the contents:
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ratings.csv: Records user interactions with books, each entry consisting of a user ID, book ID, and a rating from 1 to 5, detailing how users have rated books over time.
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to_read.csv: Lists the books users intend to read, represented as pairs of user IDs and book IDs, which helps in analyzing user preferences for future readings.
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books.csv: Contains metadata for each book, including Goodreads IDs, authors, titles, average ratings, and publication years, offering a comprehensive view of the book attributes.
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book_tags.csv: Features tags or genres assigned to books by users, showing a book's Goodreads ID, a tag ID, and the count of times the tag was applied, arranged by popularity of tags.
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tags.csv: Maps numerical tag IDs to their descriptive names, clarifying the genres or thematic elements assigned to the books by the community.
For detailed file structures and smaller examples, see the files listed above in the repository.
Note that in the notebook, due to computational resource constraints and the need to maintain manageable inference time, we selected 10% of the users with more than 20 ratings.
In this project, we introduce two novel content-based filtering methods and compare their effectiveness with established recommendation techniques, including collaborative filtering, neural collaborative filtering, matrix factorization, and graph-based algorithms. The aim is to optimize the Goodreads user experience by suggesting books tailored to their preferences.
We propose two innovative approaches to content-based filtering that utilize descriptive features of books such as titles, authors, and genres. These features are transformed into vector representations using Word2Vec for textual data and one-hot encoding for categorical data, enabling the computation of similarities between books. The new methods are benchmarked against baseline models to validate their performance in providing relevant book recommendations.
Collaborative filtering predicts user preferences based on the ratings and behaviors of similar users. It includes:
- User-User Collaborative Filtering: Identifies similar users based on ratings and recommends books based on what these similar users liked.
- Item-Item Collaborative Filtering: Recommends books by finding items similar to those the user has rated highly in the past.
NCF enhances collaborative filtering by incorporating a neural network to capture the non-linear relationships between user and item interactions. This method uses a multi-layer perceptron to learn from the features representing users and items, providing a deep learning approach to recommendation.
Matrix factorization techniques decompose the user-item interaction matrix into lower-dimensional latent factors for users and items, and they include:
- SGD-Based Matrix Factorization: Utilizes Stochastic Gradient Descent to find the optimal latent factors by minimizing the prediction error.
- ALS-Based Matrix Factorization: Uses Alternating Least Squares to optimize the latent factors in a way that alternates between fixing user factors and solving for item factors, and vice versa, suitable for large-scale data.
These methods use graph theory to improve recommendation quality, especially handling new users or items (cold-start problem) and large, sparse datasets:
- GraphSage: Generates embeddings by sampling and aggregating features from a node’s local neighborhood.
- LightGCN: Simplifies the graph convolution network by focusing on neighborhood aggregation without feature transformation and non-linear activation.
- NGCF: Integrates user-item interactions directly into the embedding process, enhancing collaborative filtering by capturing high-order connectivities.
These methods are evaluated based on their ability to accurately predict user ratings and effectively rank recommendations, employing metrics such as RMSE, MAE, and NDCG across different training and testing configurations.
The performance of our recommender system was rigorously evaluated across various configurations to ensure robustness and accuracy. We used a comprehensive set of metrics to assess the effectiveness of the system in both rating predictions and ranking quality:
- Root Mean Square Error (RMSE): Measures the average magnitude of the errors in predictions of ratings, with lower values indicating better performance.
- Mean Absolute Error (MAE): Calculates the average absolute difference between predicted and actual ratings, providing a straightforward measure of prediction accuracy.
- R-squared (R²): Indicates the proportion of variance in the dependent variable that is predictable from the independent variables, showing the strength of the relationship between predicted and actual ratings.
- Normalized Discounted Cumulative Gain (NDCG): Evaluates the ranking quality of the recommendations by considering the position of relevant items, placing more importance on higher ranks.
- Precision: Measures the ratio of recommended items that are relevant, highlighting the accuracy of the recommendation.
- Recall: Assesses the ratio of relevant items that are recommended, reflecting the system's ability to capture all relevant items.
- Mean Average Precision (MAP): Provides an average precision score across all queries, offering insight into the overall precision of the system.
These metrics were applied across different train/test split configurations to mimic real-world scenarios and challenges, particularly focusing on few-shot learning where limited data is available. By analyzing the performance under these conditions, we were able to optimize our models to deliver accurate and reliable book recommendations.
- Python 3.8 or higher
- pip (Python package installer)
python -m venv env
source env/bin/activate # On Windows use `env\Scripts\activate`git clone https://github.com/ardaxz99/Book-Recommender.git
cd goodreads-recommender
pip install -r requirements.txt
Execute the Jupyter notebook to reproduce the results:
jupyter nbconvert --to notebook --execute main.ipynb
- Arda Baris Basaran
- Louis Yann Nicolas Duval
Electrical and Electronics Engineering Department, Neuro-X Department, EPFL, Lausanne, Switzerland
This project is licensed under the MIT License - see the LICENSE.md file for details.