Synthetic sequence generator for recommender systems – memory biased random walk on a sequence multilayer network

Abstract

Personalized recommender systems rely on each user’s personal usage data in the system, in order to assist in decision making. However, privacy policies protecting users’ rights prevent these highly personal data from being publicly available to a wider researcher audience. In this work, we propose a memory biased random walk model on a multilayer sequence network, as a generator of synthetic sequential data for recommender systems. We demonstrate the applicability of the generated synthetic data in training recommender system models in cases when privacy policies restrict clickstream publishing.