Data Optimisation for a Deep Learning Recommender System

TL;DR

This study investigates how data collection restrictions affect recommender system performance and explores using secondary data sources with knowledge transfer to enhance recommendations under limited data.

Contribution

It introduces a method to measure purchase behavior similarity and demonstrates how secondary data can improve recommendation quality when data is scarce.

Findings

Performance saturates beyond a certain training data size.

The proposed similarity measure effectively identifies relevant secondary data sources.

Leveraging secondary data improves validation performance under minimal data conditions.

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 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.