837 research outputs found
Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network
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.Comment: The new updated version of the pape
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