Data Optimisation for a Deep Learning Recommender System
Gustav Hertz,
Sandhya Sachidanandan,
Balázs Tóth
et al.
Abstract:This paper advocates privacy preserving requirements on collection of user data for recommender systems. The purpose of our study is twofold. First, we ask if restrictions on data collection will hurt test quality of RNN-based recommendations. We study how validation performance depends on the available amount of training data. We use a combination of top-K accuracy, catalog coverage and novelty for this purpose, since good recommendations for the user is not necessarily captured by a traditional accuracy metr… Show more
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