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Personalized recommender systems rely on each user's personal usage data inthe system, in order to assist in decision making. However, privacy policiesprotecting users' rights prevent these highly personal data from being publiclyavailable to a wider researcher audience. In this work, we propose a memorybiased random walk model on multilayer sequence network, as a generator ofsynthetic sequential data for recommender systems. We demonstrate theapplicability of the synthetic data in training recommender system models forcases when privacy policies restrict clickstream publishing.

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