Recommendation Systems
1913 papers with code • 55 benchmarks • 56 datasets
Recommendation System in AI Research
A Recommendation System is a specialized AI-driven model that analyzes user preferences and behaviors to suggest relevant content, products, or services. It is widely used in domains like e-commerce, streaming platforms, social media, and personalized learning.
AI research in recommendation systems focuses on:
- Collaborative Filtering: Predicting user preferences based on similar users' choices.
- Content-Based Filtering: Recommending items based on user history and item characteristics.
- Hybrid Models: Combining multiple techniques for better accuracy.
- Deep Learning & Transformers: Using neural networks and self-attention mechanisms for personalized recommendations.
- Graph-Based Approaches: Leveraging knowledge graphs for relationship-aware recommendations.
Key challenges include data sparsity, scalability, and bias mitigation. Cutting-edge research explores reinforcement learning, explainability, and privacy-preserving methods to enhance recommendation systems.
Benchmarks
These leaderboards are used to track progress in Recommendation Systems
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Libraries
Use these libraries to find Recommendation Systems models and implementations
MovieLens
Netflix Prize
Gowalla
Yelp2018
ReDial
Polyvore
Subtasks
Most implemented papers
Semi-Supervised Classification with Graph Convolutional Networks
tkipf/pygcn
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We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs.
Neural Collaborative Filtering
When it comes to model the key factor in collaborative filtering -- the interaction between user and item features, they still resorted to matrix factorization and applied an inner product on the latent features of users and items.
Wide & Deep Learning for Recommender Systems
Memorization of feature interactions through a wide set of cross-product feature transformations are effective and interpretable, while generalization requires more feature engineering effort.
FiBiNET: Combining Feature Importance and Bilinear feature Interaction for Click-Through Rate Prediction
In this paper, a new model named FiBiNET as an abbreviation for Feature Importance and Bilinear feature Interaction NETwork is proposed to dynamically learn the feature importance and fine-grained feature interactions.
Session-based Recommendations with Recurrent Neural Networks
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems.
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction
xue-pai/FuxiCTR
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Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems.
Neural Graph Collaborative Filtering
xiangwang1223/neural_graph_collaborative_filtering
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Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF.
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask
We also turn the feed-forward layer in DNN model into a mixture of addictive and multiplicative feature interactions by proposing MaskBlock in this paper.
xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
On one hand, the xDeepFM is able to learn certain bounded-degree feature interactions explicitly; on the other hand, it can learn arbitrary low- and high-order feature interactions implicitly.
Deep Learning Recommendation Model for Personalization and Recommendation Systems
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks.
