Collaborative Filtering
416 papers with code • 3 benchmarks • 6 datasets
Libraries
Use these libraries to find Collaborative Filtering models and implementations
MovieLens
Gowalla
Yelp2018
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.
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.
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.
LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
gusye1234/pytorch-light-gcn
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We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.
Graph Convolutional Matrix Completion
We consider matrix completion for recommender systems from the point of view of link prediction on graphs.
A Contextual-Bandit Approach to Personalized News Article Recommendation
In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks.
Recurrent Neural Networks with Top-k Gains for Session-based Recommendations
RNNs have been shown to be excellent models for sequential data and in particular for data that is generated by users in an session-based manner.
Training Deep AutoEncoders for Collaborative Filtering
Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training.
RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems
To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance.
Knowledge Graph Convolutional Networks for Recommender Systems
hwwang55/KGCN
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To alleviate sparsity and cold start problem of collaborative filtering based recommender systems, researchers and engineers usually collect attributes of users and items, and design delicate algorithms to exploit these additional information.
