scholarly journals The Effect of Hyperparameter Selection on the Personification of Customer Population Data

2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Bernard Jansen ◽  
Soon-gyo Jung ◽  
Joni Salminen
Keyword(s):  
Matrix Factorization ◽  
Population Data ◽  
Future Research ◽  
Customer Analytics ◽  
Hyperparameter Selection ◽  
Non Negative Matrix Factorization

We explore the effects of hyperparameter selections on the personification accuracy of customer analytics data from a corporate YouTube channel with an audience in the hundreds of thousands and customer interactions in the tens of millions. Using non-negative matrix factorization, we generate personas sets from 5 to 15 using the customer analytics data, with the number of personas being the changing hyperparameter. We then compare the gender, age, nationality, and topical interests of the personas across each of the 11 persona sets using the average of the 110 generated personas as the baseline. This analysis shows that hyperparameter selection significantly alters the personification of the analytics data, with the effect most apparent with age representation. The set of 10 personas provides one of the most accurate representations across all attributes, indicating that this may be a good default hyperparameter for personification. Future research can explore other personification attributes with other customer analytics datasets.

A zero-inflated non-negative matrix factorization for the deconvolution of mixed signals of biological data

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yixin Kong ◽  
Ariangela Kozik ◽  
Cindy H. Nakatsu ◽  
Yava L. Jones-Hall ◽  
Hyonho Chun
Keyword(s):  
Matrix Factorization ◽  
Factor Model ◽  
R Package ◽  
Biological Data ◽  
Superior Performance ◽  
Sequencing Data ◽  
Fecal Microbiome ◽  
Brain Gene Expression ◽  
Cell Transcriptome ◽  
Non Negative Matrix Factorization

Abstract A latent factor model for count data is popularly applied in deconvoluting mixed signals in biological data as exemplified by sequencing data for transcriptome or microbiome studies. Due to the availability of pure samples such as single-cell transcriptome data, the accuracy of the estimates could be much improved. However, the advantage quickly disappears in the presence of excessive zeros. To correctly account for this phenomenon in both mixed and pure samples, we propose a zero-inflated non-negative matrix factorization and derive an effective multiplicative parameter updating rule. In simulation studies, our method yielded the smallest bias. We applied our approach to brain gene expression as well as fecal microbiome datasets, illustrating the superior performance of the approach. Our method is implemented as a publicly available R-package, iNMF.

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