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Hierarchical Negative Binomial Factorization for Recommender Systems on Implicit Feedback

AAAI 2021 · Probabilistic and scientific modeling › Molecules and proteins · approach: Cold-start recommendation models · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use hierarchical, factorized dispersion estimation rather than HPF's Poisson modelFastHNBF · Last.fm1KnDCG@5: 35.5±1.2 -> 52.3±0.9Helped
Use hierarchical, factorized dispersion estimation rather than HPF's Poisson modelFastHNBF · Last.fm2KnDCG@5: 16.0±0.6 -> 23.3±0.2Helped
Use hierarchical, factorized dispersion estimation rather than HPF's Poisson modelFastHNBF · Last.fm360KnDCG@5: 9.96±0.2 -> 11.1±0.2Helped
Use hierarchical, factorized dispersion estimation rather than HPF's Poisson modelFastHNBF · MovieLens100KnDCG@5: 37.5±0.9 -> 38.2±0.6Helped
Use hierarchical, factorized dispersion estimation rather than HPF's Poisson modelFastHNBF · MovieLens1MnDCG@5: 33.4±0.3 -> 33.6±0.3Helped
Use hierarchical, factorized dispersion estimation rather than HPF's Poisson modelFastHNBF · MovieLens20MnDCG@5: 30.0±0.3 -> 30.0±0.3No clear effect
Use hierarchical, factorized dispersion estimation rather than NBF's single-layer dispersion modelFastHNBF · Jester2nDCG@5: 41.1±1.0 -> 41.9±0.7Helped
Use hierarchical, factorized dispersion estimation rather than WMF's Gaussian exposure modelFastHNBF · EachMovienDCG@5: 50.7±0.7 -> 47.8±0.6Hurt
Replace entry-wise dispersion for zero entries with factorized dispersionFastHNBF · implicit feedbackupdating time complexity: O(MN) -> O(|X +|)Helped

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