Abstract:
Recommender systems play a crucial role in enhancing user experience and facilitating personalized recommendations in various domains, such as e-commerce, social media, and entertainment. In recent years, deep learning techniques have gained significant attention for building effective recommender systems due to their ability to capture complex patterns and representations in user-item interactions.
This paper focuses on two prominent deep learning models for recommender systems: Neural Collaborative Filtering (NCF) and Autoencoder-Enabled models. NCF is a hybrid model that combines collaborative filtering techniques with neural networks, enabling effective representation learning for personalized recommendations. The model leverages the power of matrix factorization and deep neural networks to capture both explicit and implicit user-item interactions.
Autoencoder-Enabled models, on the other hand, utilize autoencoders, a type of unsupervised neural network, for learning low-dimensional representations of user-item interactions. These models can reconstruct the input data by compressing it into a bottleneck layer and then decoding it back to its original form. By leveraging the reconstruction error, these models can effectively capture latent features and generate personalized recommendations.
Both NCF and Autoencoder-Enabled models have demonstrated remarkable performance improvements over traditional collaborative filtering methods. They address the limitations of traditional approaches by leveraging the power of deep learning to capture complex user-item relationships, handle sparsity in data, and provide highly accurate and personalized recommendations.
In this paper, we discuss the architecture, training process, and key components of NCF and Autoencoder-Enabled models. We review state-of-the-art techniques and advancements in these models, including variations such as convolutional neural network-based NCF and denoising autoencoders. Furthermore, we explore the challenges and opportunities associated with these models, including scalability, interpretability, and cold-start problems.
Overall, this paper provides a comprehensive overview of Neural Collaborative Filtering and Autoencoder-Enabled deep learning models for recommender systems. These models have shown great potential in improving recommendation accuracy and personalization, thereby enhancing user satisfaction and engagement in various domains. Future research directions and potential applications of these models are also discussed, highlighting the importance of continued exploration and refinement of deep learning techniques for recommender systems.
NEURAL COLLABORATIVE FILTERING AND AUTOENCODER ENABLED DEEP LEARNING MODELS FOR RECOMMENDER SYSTEMS. GET MOREÂ COMPUTER SCIENCE PROJECT TOPICS AND MATERIALS
