Collaborative Filtering On Movielens 20M

Metrics

HR@10

nDCG@10

Results

Performance results of various models on this benchmark

			Paper Title
LRML	0.8447	0.6152	Latent Relational Metric Learning via Memory-based Attention for Collaborative Ranking
HyperML	0.8736	0.6404	HyperML: 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	-	0.1195	Hierarchical 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
CML	0.7764	0.5301	Collaborative 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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