Collaborative Filtering

269 papers with code • 1 benchmarks • 2 datasets

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Use these libraries to find Collaborative Filtering models and implementations

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.

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.

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.

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.

A Contextual-Bandit Approach to Personalized News Article Recommendation

ray-project/ray 28 Feb 2010

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.

LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation

gusye1234/pytorch-light-gcn 6 Feb 2020

We propose a new model named LightGCN, including only the most essential component in GCN -- neighborhood aggregation -- for collaborative filtering.

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

hwwang55/RippleNet 9 Mar 2018

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.

Training Deep AutoEncoders for Collaborative Filtering

NVIDIA/DeepRecommender 5 Aug 2017

Our model is based on deep autoencoder with 6 layers and is trained end-to-end without any layer-wise pre-training.

Knowledge Graph Convolutional Networks for Recommender Systems

hwwang55/KGCN 18 Mar 2019

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.

Item2Vec: Neural Item Embedding for Collaborative Filtering

massquantity/LibRecommender 14 Mar 2016

Many Collaborative Filtering (CF) algorithms are item-based in the sense that they analyze item-item relations in order to produce item similarities.