Collaborative Filtering on Gowalla

11개 결과 · ⬇ CSV · JSON

Recall@20

Rank	Model	Recall@20	NDCG@20	Paper	Code	Year
1	RLAE-DAN	0.1922	0.1605	Why is Normalization Necessary for Linear Recommenders?	psm1206/dan	2025
2	BSPM-EM	0.1920	0.1597	Blurring-Sharpening Process Models for Collaborative Filtering	jeongwhanchoi/bspm	2022
3	NESCL	0.1917	0.1617	Neighborhood-Enhanced Supervised Contrastive Learning for Collaborative Filtering	PeiJieSun/NESCL	2024
4	BSPM-LM	0.1901	0.1570	Blurring-Sharpening Process Models for Collaborative Filtering	jeongwhanchoi/bspm	2022
5	LT-OCF	0.1875	0.1574	LT-OCF: Learnable-Time ODE-based Collaborative Filtering	jeongwhanchoi/lt-ocf · jeongwhanchoi/LT-OCF-Tutorial	2021
6	SimpleX	0.1872	0.1557	SimpleX: A Simple and Strong Baseline for Collaborative Filtering	reczoo/RecZoo	2021
7	UltraGCN	0.1862	0.1580	UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation	reczoo/RecZoo · shuyao-wang/dsl	2021
7	Emb-GCN	0.1862	–	UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation	reczoo/RecZoo · shuyao-wang/dsl	2021
9	GF-CF	0.1849	0.1518	How Powerful is Graph Convolution for Recommendation?	yshenaw/GF_CF	2021
10	LightGCN	0.1830	0.1554	LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation	microsoft/recommenders · gusye1234/pytorch-light-gcn · PreferredAI/cornac · +15	2020
11	NGCF	0.1570	–	Neural Graph Collaborative Filtering	dmlc/dgl · PreferredAI/cornac · xiangwang1223/neural_graph_collaborative_filtering · +18	2019