Collaborative Filtering
374 papers with code • 1 benchmarks • 4 datasets
Libraries
Use these libraries to find Collaborative Filtering models and implementations
Gowalla
Yelp2018
Most implemented papers
A Neural Autoregressive Approach to Collaborative Filtering
This paper proposes CF-NADE, a neural autoregressive architecture for collaborative filtering (CF) tasks, which is inspired by the Restricted Boltzmann Machine (RBM) based CF model and the Neural Autoregressive Distribution Estimator (NADE).
StarSpace: Embed All The Things!
A framework for training and evaluating AI models on a variety of openly available dialogue datasets.
Variational Autoencoders for Collaborative Filtering
We introduce a generative model with multinomial likelihood and use Bayesian inference for parameter estimation.
HybridSVD: When Collaborative Information is Not Enough
We propose a new hybrid algorithm that allows incorporating both user and item side information within the standard collaborative filtering technique.
Local Popularity and Time in top-N Recommendation
Items popularity is a strong signal in recommendation algorithms.
A Hybrid Variational Autoencoder for Collaborative Filtering
Our approach combines movie embeddings (learned from a sibling VAE network) with user ratings from the Movielens 20M dataset and applies it to the task of movie recommendation.
Algorithm Selection for Collaborative Filtering: the influence of graph metafeatures and multicriteria metatargets
However, the results have shown that the feature selection procedure used to create the comprehensive metafeatures is is not effective, since there is no gain in predictive performance.
NAIS: Neural Attentive Item Similarity Model for Recommendation
AaronHeee/Neural-Attentive-Item-Similarity-Model
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As such, the key to an item-based CF method is in the estimation of item similarities.
Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation
hwwang55/MKR
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Collaborative filtering often suffers from sparsity and cold start problems in real recommendation scenarios, therefore, researchers and engineers usually use side information to address the issues and improve the performance of recommender systems.
Joint Neural Collaborative Filtering for Recommender Systems
MTC-ETH/RecommenderSystems
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Deep feature learning extracts feature representations of users and items with a deep learning architecture based on a user-item rating matrix.
