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Explaining reviews and ratings with PACO: Poisson Additive Co-Clustering
[article]
2015
arXiv
pre-print
Understanding a user's motivations provides valuable information beyond the ability to recommend items. Quite often this can be accomplished by perusing both ratings and review texts, since it is the latter where the reasoning for specific preferences is explicitly expressed. Unfortunately matrix factorization approaches to recommendation result in large, complex models that are difficult to interpret and give recommendations that are hard to clearly explain to users. In contrast, in this
arXiv:1512.01845v1
fatcat:3xixuzue3zbwlfccmvijpgi5fu