Data Optimisation for a Deep Learning Recommender System
This work addresses privacy concerns in recommender systems by exploring data optimization strategies, but it is incremental as it builds on existing methods for knowledge transfer and data analysis.
The paper investigates how data restrictions affect RNN-based recommender system performance, finding that test performance saturates beyond a critical training size, and proposes a knowledge transfer method using a representation to measure purchase behavior similarities, which improves validation performance when selecting relevant source domains.
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 metric. Second, we ask if we can improve the quality under minimal data by using secondary data sources. We propose knowledge transfer for this purpose and construct a representation to measure similarities between purchase behaviour in data. This to make qualified judgements of which source domain will contribute the most. Our results show that (i) there is a saturation in test performance when training size is increased above a critical point. We also discuss the interplay between different performance metrics, and properties of data. Moreover, we demonstrate that (ii) our representation is meaningful for measuring purchase behaviour. In particular, results show that we can leverage secondary data to improve validation performance if we select a relevant source domain according to our similarly measure.