Recommendation Systems
1234 papers with code • 48 benchmarks • 44 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 )
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
Gowalla
Netflix Prize
MIND
ReDial
Yelp2018
Polyvore
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
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.
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.
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.
Graph Convolutional Matrix Completion
We consider matrix completion for recommender systems from the point of view of link prediction on graphs.
AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks
DeepGraphLearning/RecommenderSystems
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Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space.
