Introduction
The project delves into the significance of recommendation systems in various online platforms, emphasizing their role in user experiences, revenue generation, and market differentiation. The focus is on understanding recommender systems' evolution and impact on user engagement and satisfaction.
Data
Utilized the Book Recommendation dataset containing anonymized user information, book details, and ratings. Preprocessing involved handling missing values, dropping unnecessary columns, and setting rating count thresholds for data quality. Exploratory Data Analysis (EDA) offered insights into book popularity, rating distributions, and user engagement patterns.
Methodology
Implemented a Hybrid Collaborative-Content filtering model using Python libraries like Pandas, NumPy, and Scikit-Learn. The model leveraged collaborative filtering (user-item interactions) and content-based filtering (book characteristics) to provide personalized recommendations. Thresholds were applied to refine the dataset for better reliability.
Results
Evaluated the models' accuracy using metrics like RMSE, precision, recall, and F1-score. Collaborative filtering showed an RMSE of 3.51, content-based filtering at 3.79, while the hybrid model achieved an RMSE of approximately 4.64. The hybrid model, combining both approaches, displayed improved precision, recall, and overall recommendation quality.
Conclusion
The project aimed to build an effective recommendation system by combining collaborative and content-based filtering techniques. It highlighted the model's capabilities in overcoming challenges like dataset sparsity and the cold start problem. Future improvements might focus on enhancing the hybrid model's accuracy further or exploring other advanced techniques.