Recommendation Systems on Amazon-Book

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nDCG@20

Rank	Model	nDCG@20	Recall@20	HR@10	NDCG@10	HR@50	NDCG@50	Paper	Code	Year
1	SSCF	0.0647	0.0773	–	–	–	–	Sapling Similarity: a performing and interpretable memory-based tool for recommendation	giamba95/saplingsimilarity	2022
2	SANSA	0.0637	0.0768	–	–	–	–	Scalable Approximate NonSymmetric Autoencoder for Collaborative Filtering	glami/sansa	2023
3	RLAE-DAN	0.0630	0.0762	–	–	–	–	Why is Normalization Necessary for Linear Recommenders?	psm1206/dan	2025
4	BSPM-LM	0.0610	0.0733	–	–	–	–	Blurring-Sharpening Process Models for Collaborative Filtering	jeongwhanchoi/bspm	2022
5	BSPM-EM	0.0609	0.0733	–	–	–	–	Blurring-Sharpening Process Models for Collaborative Filtering	jeongwhanchoi/bspm	2022
6	Turbo-CF	0.0574	0.0693	–	–	–	–	Turbo-CF: Matrix Decomposition-Free Graph Filtering for Fast Recommendation	jindeok/turbo-cf	2024
7	Emb-GCN	0.0556	0.0681	–	–	–	–	UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation	reczoo/RecZoo · shuyao-wang/dsl	2021
8	NESCL	0.0513	0.0624	–	–	–	–	Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering	PeiJieSun/NESCL	2024
9	SimpleX	0.0468	0.0583	–	–	–	–	SimpleX: A Simple and Strong Baseline for Collaborative Filtering	reczoo/RecZoo	2021
10	MGDCF	0.0460	0.0566	–	–	–	–	MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering	CrawlScript/Torch-MGDCF · hujunxianligong/MGDCF	2022
11	LT-OCF	0.0341	0.0442	–	–	–	–	LT-OCF: Learnable-Time ODE-based Collaborative Filtering	jeongwhanchoi/lt-ocf · jeongwhanchoi/LT-OCF-Tutorial	2021
12	LightGCN	0.0315	0.0411	–	–	–	–	LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation	microsoft/recommenders · gusye1234/pytorch-light-gcn · PreferredAI/cornac · +15	2020
13	NGCF	0.0263	0.0344	–	–	–	–	Neural Graph Collaborative Filtering	dmlc/dgl · PreferredAI/cornac · xiangwang1223/neural_graph_collaborative_filtering · +18	2019
14	HSTU+MoL	–	–	0.0613	0.0350	0.1292	0.0498	Retrieval with Learned Similarities	bailuding/rails	2024
15	HSTU	–	–	0.0478	0.0262	0.1082	0.0393	Actions Speak Louder than Words: Trillion-Parameter Sequential Transducers for Generative Recommendations	facebookresearch/generative-recommenders · alibaba/TorchEasyRec · glb400/Toy-RecLM · +9	2024
16	SASRec	–	–	0.0306	0.0164	0.0754	0.0260	Self-Attentive Sequential Recommendation	microsoft/recommenders · facebookresearch/generative-recommenders · kang205/SASRec · +5	2018