Recommendation Systems On Amazon Book

Métriques

Recall@20

nDCG@20

Résultats

Résultats de performance de divers modèles sur ce benchmark

			Paper Title
SSCF	0.0773	0.0647	Sapling Similarity: a performing and interpretable memory-based tool for recommendation
SANSA	0.0768	0.0637	Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering
BSPM-LM	0.0733	0.0610	Blurring-Sharpening Process Models for Collaborative Filtering
BSPM-EM	0.0733	0.0609	Blurring-Sharpening Process Models for Collaborative Filtering
Turbo-CF	0.0693	0.0574	Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation
Emb-GCN	0.0681	0.0556	UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
NESCL	0.0624	0.0513	Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering
SimpleX	0.0583	0.0468	SimpleX: A Simple and Strong Baseline for Collaborative Filtering
MGDCF	0.0566	0.0460	MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering
LT-OCF	0.0442	0.0341	LT-OCF: Learnable-Time ODE-based Collaborative Filtering
LightGCN	0.0411	0.0315	LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation
NGCF	0.0344	0.0263	Neural Graph Collaborative Filtering
HSTU+MoL	-	-	Retrieval with Learned Similarities
SASRec	-	-	Self-Attentive Sequential Recommendation
HSTU	-	-	Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations

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Recommendation Systems On Amazon Book | SOTA | HyperAI