Hybrid Book Recommendation System Using Content-Based Filtering and Collaborative Filtering Based on Singular Value Decomposition
- Keywords:
- recommendation system, collaborative filtering, content-based filtering, SVD, TF-IDF, machine learning, hybrid recommendation
- Abstract
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Introduction: The rapid growth in the number of digital books on platforms such as Goodreads, Google Books, and Amazon has led to information overload and a paradox of choice for readers. The book recommendation system is an important solution to provide personalized and relevant advice.
Objective: This study aims to develop a hybrid book recommendation system using content-based filtering and collaborative filtering based on singular value decomposition.
Methods: This study developed a hybrid book recommendation system that combines TF-IDF-based Content-Based Filtering with Collaborative Filtering based on Singular Value Decomposition (SVD). The Goodbooks-10k dataset (10,000 books, 981,756 ratings from 53,424 unique users) was used in this study. In Content-Based Filtering, text features are extracted from a combination of tags, titles, and authors using the TF-IDF Vectorizer (max_features = 5,000, ngram_range = (1,2)) and similarity is calculated by cosine similarity. Collaborative Filtering uses SVD with 50 latent factors on the normalized user-item matrix (14,639 × 9,999).
Results: The results of the evaluation showed that Content-Based Filtering had a diversity of 0.7250 but low coverage (0.0029) due to popularity bias, while SVD-based Collaborative Filtering obtained an RMSE of 3.5613 and MAE of 3.3896 in 1,000 random test samples.
Conclusion: The hybrid system developed can overcome the limitations of each single method to produce more accurate, personalized, and diverse recommendations. This research contributes to the development of a computationally efficient digital literature recommendation system. - Downloads
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- 2025-12-31 — Updated on 2026-01-02
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