Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network
This addresses privacy constraints in recommender system development for researchers and practitioners, though it appears incremental as it builds on existing random walk and synthetic data generation methods.
The authors tackled the problem of limited access to personal user data for recommender system research due to privacy policies by proposing a memory biased random walk model on a multilayer sequence network to generate synthetic sequential data, demonstrating its applicability in training recommender models when clickstream data cannot be published.
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 multilayer sequence network, as a generator of synthetic sequential data for recommender systems. We demonstrate the applicability of the synthetic data in training recommender system models for cases when privacy policies restrict clickstream publishing.