Data Optimisation for a Deep Learning Recommender System

by   Gustav Hertz, et al.

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.


page 1

page 2

page 3

page 4


PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation

Transfer learning is an effective technique to improve a target recommen...

Limits to Surprise in Recommender Systems

In this study, we address the challenge of measuring the ability of a re...

Differential Private Knowledge Transfer for Privacy-Preserving Cross-Domain Recommendation

Cross Domain Recommendation (CDR) has been popularly studied to alleviat...

PPGenCDR: A Stable and Robust Framework for Privacy-Preserving Cross-Domain Recommendation

Privacy-preserving cross-domain recommendation (PPCDR) refers to preserv...

Recommending Burgers based on Pizza Preferences: Addressing Data Sparsity with a Product of Experts

In this paper we describe a method to tackle data sparsity and create re...

Aligning Intraobserver Agreement by Transitivity

Annotation reproducibility and accuracy rely on good consistency within ...

Please sign up or login with your details

Forgot password? Click here to reset