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.

Libraries

Use these libraries to find Recommendation Systems models and implementations
36 papers
686
26 papers
7,779
24 papers
1,128
See all 9 libraries.

Most implemented papers

Semi-Supervised Classification with Graph Convolutional Networks

tkipf/pygcn 9 Sep 2016

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

hexiangnan/neural_collaborative_filtering WWW 2017

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

microsoft/recommenders 24 Jun 2016

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

shenweichen/DeepCTR 23 May 2019

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

microsoft/recommenders 21 Nov 2015

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 13 Mar 2017

Learning sophisticated feature interactions behind user behaviors is critical in maximizing CTR for recommender systems.

Neural Graph Collaborative Filtering

xiangwang1223/neural_graph_collaborative_filtering 20 May 2019

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

twitter/the-algorithm 9 Feb 2021

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

Leavingseason/xDeepFM 14 Mar 2018

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

facebookresearch/dlrm 31 May 2019

With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks.