# Recommendation Systems

850 papers with code • 45 benchmarks • 37 datasets

The recommendation systems task is to produce a list of recommendations for a user. The most common methods used in recommender systems are factor models (Koren et al., 2009; Weimer et al., 2007; Hidasi & Tikk, 2012) and neighborhood methods (Sarwar et al., 2001; Koren, 2008). Factor models work by decomposing the sparse user-item interactions matrix to a set of d dimensional vectors one for each item and user in the dataset. Factor models are hard to apply in session-based recommendations due to the absence of a user profile. On the other hand, neighborhood methods, which rely on computing similarities between items (or users) are based on co-occurrences of items in sessions (or user profiles). Neighborhood methods have been used extensively in session-based recommendations.

( Image credit: CuMF_SGD )

## Libraries

Use these libraries to find Recommendation Systems models and implementations## Most implemented papers

# 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.

# 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

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

# 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.

# Variational Autoencoders for Collaborative Filtering

This non-linear probabilistic model enables us to go beyond the limited modeling capacity of linear factor models which still largely dominate collaborative filtering research. We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation.

# 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.

# Graph Convolutional Matrix Completion

We consider matrix completion for recommender systems from the point of view of link prediction on graphs.

# Neural Graph Collaborative Filtering

Further analysis verifies the importance of embedding propagation for learning better user and item representations, justifying the rationality and effectiveness of NGCF.

# AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks

Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space.