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Privacy Preserving Point-of-Interest Recommendation Using Decentralized Matrix Factorization

AAAI 2018 · Federated learning and privacy › Differential privacy · approach: Differentially private clustering · 72 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add personal (local) item factor to global-only DMF (GDMF -> DMF) (ablation)DMF · Foursquare K=5R@5: 0.1245 -> 0.1562Helped
add personal (local) item factor to global-only DMF (GDMF -> DMF) (ablation)DMF · Alipay K=5R@5: 0.0937 -> 0.1128Helped
add gradient exchange among neighbors to local-only DMF (LDMF -> DMF) (ablation)DMF · Foursquare K=5R@5: 0.0125 -> 0.1562Helped
add gradient exchange among neighbors to local-only DMF (LDMF -> DMF) (ablation)DMF · Alipay K=5R@5: 0.0042 -> 0.1128Helped
decentralized DMF vs centralized MFMF · Alipay K=5P@5 / R@5: +27.75% P@5, +25.89% R@5Helped
DMF vs pairwise-loss BPRBPR · Foursquare/Alipay K=15P@5: Foursquare K=15 P@5 0.0448 -> 0.0421Mixed
increase random walk distance D (ablation)DMF · Foursquare and Alipay K=5precision/recallHelped
increase latent dimension K from 5 to 15all models · Foursquare/AlipayP@k, R@k: DMF Foursquare R@10 0.2418 -> 0.3035Helped
tune global/local regularizers beta, gamma to balance own and neighbor data (ablation)DMF · Foursquare and Alipayprecision/recallHelped

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