Recommendation Systems on Gowalla

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nDCG@20

Rank	Model	nDCG@20	Recall@20	HR@10	HR@100	PSP@10	nDCG@10	nDCG@100	Paper	Code	Year
1	ConvNCF	0.6019	–	–	–	–	–	–	Outer Product-based Neural Collaborative Filtering	duxy-me/ConvNCF	2018
2	NESCL	0.1617	0.1917	–	–	–	–	–	Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering	PeiJieSun/NESCL	2024
3	RLAE-DAN	0.1605	0.1922	–	–	–	–	–	Why is Normalization Necessary for Linear Recommenders?	psm1206/dan	2025
4	BSPM-EM	0.1597	0.1920	–	–	–	–	–	Blurring-Sharpening Process Models for Collaborative Filtering	jeongwhanchoi/bspm	2022
5	MGDCF	0.1589	0.1864	–	–	–	–	–	MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering	CrawlScript/Torch-MGDCF · hujunxianligong/MGDCF	2022
6	Emb-GCN	0.1580	0.1862	–	–	–	–	–	UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation	reczoo/RecZoo · shuyao-wang/dsl	2021
7	LT-OCF	0.1574	0.1875	–	–	–	–	–	LT-OCF: Learnable-Time ODE-based Collaborative Filtering	jeongwhanchoi/lt-ocf · jeongwhanchoi/LT-OCF-Tutorial	2021
8	BSPM-LM	0.157	0.1901	–	–	–	–	–	Blurring-Sharpening Process Models for Collaborative Filtering	jeongwhanchoi/bspm	2022
9	SimpleX	0.1557	0.1872	–	–	–	–	–	SimpleX: A Simple and Strong Baseline for Collaborative Filtering	reczoo/RecZoo	2021
10	LightGCN	0.1554	0.1830	–	–	–	–	–	LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation	microsoft/recommenders · gusye1234/pytorch-light-gcn · PreferredAI/cornac · +15	2020
11	SSCF	0.1390	0.1775	–	–	–	–	–	Sapling Similarity: a performing and interpretable memory-based tool for recommendation	giamba95/saplingsimilarity	2022
12	NGCF	0.1327	0.1570	–	–	–	–	–	Neural Graph Collaborative Filtering	dmlc/dgl · PreferredAI/cornac · xiangwang1223/neural_graph_collaborative_filtering · +18	2019
13	SVD-AE	–	–	0.144	0.3734	0.248	0.1394	0.2115	SVD-AE: Simple Autoencoders for Collaborative Filtering	seoyoungh/svd-ae · jeongwhanchoi/svd-ae	2024