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 )


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Most implemented papers

Neural Collaborative Filtering

microsoft/recommenders 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.

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.

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.

Variational Autoencoders for Collaborative Filtering

dawenl/vae_cf 16 Feb 2018

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

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.

Graph Convolutional Matrix Completion

riannevdberg/gc-mc 7 Jun 2017

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

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.

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

DeepGraphLearning/RecommenderSystems 29 Oct 2018

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