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Found 4 papers, 3 papers with code
A Comparative Study of Gamma Markov Chains for Temporal Non-Negative Matrix Factorization
Non-negative matrix factorization (NMF) has become a well-established class of methods for the analysis of non-negative data.
Ordinal Non-negative Matrix Factorization for Recommendation
In particular, our algorithm preserves the scalability of PF and can be applied to huge sparse datasets.
Recommendation from Raw Data with Adaptive Compound Poisson Factorization
Count data are often used in recommender systems: they are widespread (song play counts, product purchases, clicks on web pages) and can reveal user preference without any explicit rating from the user.
Negative Binomial Matrix Factorization for Recommender Systems
We introduce negative binomial matrix factorization (NBMF), a matrix factorization technique specially designed for analyzing over-dispersed count data.
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