Summary of A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systems, by Pang Li et al.
A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systems
by Pang Li, Shahrul Azman Mohd Noah, Hafiz Mohd Sarim
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey examines the integration of Deep Neural Networks (DNNs) into Collaborative Filtering (CF) recommendation systems. Traditional CF methods face limitations, such as scalability issues, which DNNs can address by modeling complex relationships within data. The paper first reviews fundamental principles of CF and deep learning, setting the stage for understanding their combination. Next, it categorizes various DNN models that enhance CF systems, including Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), autoencoders, Generative Adversarial Networks (GAN), and Restricted Boltzmann Machines (RBM). The survey also discusses evaluation protocols, publicly available data features, and auxiliary information. Finally, it highlights challenges and future research opportunities in improving CF systems with deep learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can better recommend things you might like by using special kinds of artificial intelligence called Deep Neural Networks (DNNs). Right now, computer recommendation systems are limited because they don’t understand complex relationships between different pieces of information. DNNs can help solve this problem by finding patterns in the data that humans can’t see. The paper talks about the basics of how computers recommend things and then explains some advanced techniques using DNNs to make these recommendations even better. It also discusses how we evaluate how well these systems work and what areas need more improvement. |
Keywords
» Artificial intelligence » Cnn » Deep learning » Gan » Gnn » Rnn
