CF-KAN

KAN (Kolmogorov-Arnold Network)-based Recommender System (Collaborative Filtering (CF))

CF-KAN is a recommendation method (collaborative filtering) based on the Kolmogorov-Arnold Network (KAN) approach. This research leverages and explores the power of KAN for CF-based recommendation.

** Our paper (CF-KAN) is now available at arXiv: https://www.arxiv.org/abs/2409.05878**

Running

python main.py

The Results (Recommendation Accuracy)

We comprehensively compare CF-KAN with the following categories of recommendation models: Matrix factorization-based methods: MF-BPR, NeuMF, and DMF; Autoencoder-based methods: CDAE, Multi-DAE, and RecVAE; GCN-based methods: SpectralCF, NGCF ,LightGCN, SGL, and NCL; Generative model-based methods: CFGAN, RecVAE, and DiffRec.

Method	ML-1M				Yelp				Anime
	R@10	R@20	N@10	N@20	R@10	R@20	N@10	N@20	R@10	R@20	N@10	N@20
MF-BPR	0.0876	0.1503	0.0749	0.0966	0.0341	0.0560	0.0210	0.0341	0.1521	0.2449	0.2925	0.3153
NeuMF	0.0845	0.1465	0.0759	0.0965	0.0378	0.0637	0.0230	0.0308	0.1531	0.2442	0.3277	0.3259
DMF	0.0799	0.1368	0.0731	0.0921	0.0342	0.0588	0.0208	0.0282	0.1386	0.2161	0.3277	0.3122
CDAE	0.0991	0.1705	0.0829	0.1078	0.0444	0.0703	0.0280	0.0360	0.2031	0.2845	0.4652	0.4301
MultiDAE	0.0975	0.1707	0.0820	0.1046	0.0531	0.0876	0.0316	0.0421	0.2022	0.2882	0.4577	0.4125
RecVAE	0.0835	0.1422	0.0769	0.0963	0.0493	0.0824	0.0303	0.0403	0.2137	0.3068	0.4105	0.4068
SpectralCF	0.0751	0.1291	0.0740	0.0909	0.0368	0.0572	0.0201	0.0298	0.1633	0.2564	0.3102	0.3236
NGCF	0.0864	0.1484	0.0805	0.1038	0.0417	0.0708	0.0255	0.0346	0.2071	0.3043	0.3937	0.3827
LightGCN	0.0824	0.1419	0.0793	0.1006	0.0505	0.0858	0.0312	0.0417	0.2071	0.3043	0.3937	0.3827
SGL	0.0885	0.1575	0.0802	0.1029	0.0564	0.0944	0.0346	0.0462	0.1994	0.2918	0.3748	0.3652
NCL	0.0878	0.1471	0.0819	0.1011	0.0535	0.0906	0.0326	0.0438	0.2063	0.3047	0.3919	0.3819
CFGAN	0.0684	0.1181	0.0663	0.0828	0.0206	0.0347	0.0129	0.0172	0.1946	0.2889	0.4601	0.4289
DiffRec	0.1021	0.1763	0.0877	0.1131	0.0554	0.0914	0.0345	0.0452	0.2104	0.3012	0.5047	0.4649
CF-KAN	0.1065	0.1831	0.0894	0.1152	0.0590	0.0961	0.0359	0.0471	0.2287	0.3261	0.5256	0.4875

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Citation

If our work was helpful for your project, cite our work :)

@article{park2024cf,

title={CF-KAN: Kolmogorov-Arnold Network-based Collaborative Filtering to Mitigate Catastrophic Forgetting in Recommender Systems},

author={Park, Jin-Duk and Kim, Kyung-Min and Shin, Won-Yong},

journal={arXiv preprint arXiv:2409.05878},

year={2024} }

Etc.

For the implementation of KANs, I've referred to the following repos: EfficentKAN, Visualization API