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A Differentiable Ranking Metric Using Relaxed Sorting Operation for Top-K Recommender Systems [article]

Hyunsung Lee, Yeongjae Jang, Jaekwang Kim, Honguk Woo
2020 arXiv   pre-print
A recommender system generates personalized recommendations for a user by computing the preference score of items, sorting the items according to the score, and filtering top-K items with high scores.  ...  In this work, we present DRM (differentiable ranking metric) that mitigates the inconsistency and improves recommendation performance by employing the differentiable relaxation of ranking metrics.  ...  hence improve the performance of top-K recommendations. We show that the DRM objective is readily incorporated into the existing factor based recommenders via joint learning.  ... 
arXiv:2008.13141v4 fatcat:bgxf3itixjbypblycbng3nvnuq


G «continent», Str Raţiu, Nr, Dan Popescu
2010 unpublished
For both regressions, we see that R-squared is very close to 1 (0,999501 for the first and 0,999312 for the second), and the p-value for the F-statistics is very close to 0 (4,07e-65 for the first, and  ...  8,37e-55 for the second); this means that the data are well adjusted by the equations.  ...  …after a whole decade of cocktails, we became very relaxed about all sorts of risks!". (Posner, B.G., Hopkins, M.S., 2009, p. 56) We believe nothing can happen to us!  ...