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Recommender systems (RS) are essential for generating personalized suggestions based on user preferences, historical interactions, and item attributes. These systems enhance user experience by helping individuals discover relevant content, such as movies, music, books, or products tailored to their interests. Popular platforms like Netflix, Amazon, and YouTube leverage RS to deliver high-quality recommendations that improve content discovery and user satisfaction. Collaborative Filtering (CF), a widely used technique, analyzes user-item interactions to identify patterns and similarities. However, CF faces challenges such as scalability, data sparsity, and the cold-start problem, which limit its effectiveness. Addressing these issues is crucial for improving recommendation accuracy and ensuring consistent performance.
Research on RS has increasingly incorporated advanced deep learning (DL) techniques to overcome traditional limitations. Studies have explored various approaches, such as CNNs, RNNs, and hybrid models, that combine collaborative filtering with DL architectures. Techniques like autoencoders, GNNs, and reinforcement learning have also been applied to improve recommendation relevance and adaptability. Recent works focus on privacy-aware RS, multimodal analysis, and time-sensitive recommendations, demonstrating the potential of DL to handle sparse data, enhance personalization, and adapt to dynamic user preferences. These innovations address critical gaps in RS, paving the way for more efficient and user-centric recommendation systems.
Researchers from Mansoura University have introduced the HRS-IU-DL model, an advanced hybrid recommendation system that integrates multiple techniques to enhance accuracy and relevance. The model combines user-based and item-based CF with Neural Collaborative Filtering (NCF) to capture non-linear relationships, RNN for sequential pattern analysis, and CBF using TF-IDF for detailed item attribute evaluation. Evaluated on the Movielens 100k dataset, the model demonstrates superior performance across metrics like RMSE, MAE, Precision, and Recall, addressing challenges such as data sparsity and the cold-start problem while significantly advancing recommendation system technologies.
The study enhances RS by integrating NCF with CF and combining RNN with Content-Based Filtering (CBF). The hybrid model (HRS-IU-DL) leverages user-item interactions, item attributes, and sequential patterns for accurate, personalized recommendations. Using the Movielens dataset, the approach incorporates matrix factorization, cosine similarity, and TF-IDF for feature extraction, alongside deep learning techniques to address cold-start and data sparsity challenges. Privacy-preserving methods ensure user data security. The model effectively captures complex user behaviors and temporal dynamics, improving recommendation accuracy and diversity across e-commerce, entertainment, and online platforms.
The proposed hybrid model (HRS-IU-DL) was evaluated on the Movielens 100k dataset, split 80–20 for training and testing, and compared against baseline models. Initial data exploration included rating distribution and statistical analysis to address sparsity and imbalance—preprocessing steps involved normalization, privacy-preserving techniques, and filtering user and movie IDs. The model combines CF, NCF, CBF, and RNN to leverage user-item interactions and item properties. Hyperparameter tuning enhanced performance metrics, achieving RMSE of 0.7723, MAE of 0.6018, Precision of 0.8127, and Recall of 0.7312. It outperformed baseline models in accuracy and efficiency, demonstrating superior recommendation capabilities.
In conclusion, the HRS-IU-DL hybrid model integrates CF, CBF, NCF, and RNN to improve recommendation accuracy by addressing limitations like data sparsity and the cold-start problem. The system delivers personalized recommendations by leveraging user-item interactions and item properties. Experiments on the Movielens 100k dataset highlight its superior performance, achieving the lowest RMSE and MAE alongside improved Precision and Recall. Future research will incorporate advanced architectures like Transformers, contextual data, and test scalability on larger datasets. Efforts will also focus on enhancing computational efficiency and scalability for real-world applications.
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“}]] [[{“value”:”Recommender systems (RS) are essential for generating personalized suggestions based on user preferences, historical interactions, and item attributes. These systems enhance user experience by helping individuals discover relevant content, such as movies, music, books, or products tailored to their interests. Popular platforms like Netflix, Amazon, and YouTube leverage RS to deliver high-quality recommendations that improve
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