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Collaborative Filtering On Movielens 20M

Metrics

HR@10
nDCG@10

Results

Performance results of various models on this benchmark

Paper Title
LRML0.84470.6152Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
HyperML0.87360.6404HyperML: A Boosting Metric Learning Approach in Hyperbolic Space for Recommender Systems
GRU4Rec--Session-based Recommendations with Recurrent Neural Networks
KGNN-LS--Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems
HGN-0.1195Hierarchical Gating Networks for Sequential Recommendation
EASE--Embarrassingly Shallow Autoencoders for Sparse Data
SASRec--Self-Attentive Sequential Recommendation
RaCT--Towards Amortized Ranking-Critical Training for Collaborative Filtering
Mult-DAE--Variational Autoencoders for Collaborative Filtering
RecVAE--RecVAE: a New Variational Autoencoder for Top-N Recommendations with Implicit Feedback
BERT4Rec--BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
Mult-VAE PR--Variational Autoencoders for Collaborative Filtering
HSTU--Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations
CML0.77640.5301Collaborative Metric Learning
H+Vamp Gated--Enhancing VAEs for Collaborative Filtering: Flexible Priors & Gating Mechanisms
LED--Lightweight representation learning for efficient and scalable recommendation
Multi-Gradient Descent--Multi-Gradient Descent for Multi-Objective Recommender Systems
VASP--Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)
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