Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network
Information Retrieval
2015-03-20 v2 Computers and Society
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 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.
Cite
@article{arxiv.1201.6134,
title = {Synthetic sequence generator for recommender systems - memory biased random walk on sequence multilayer network},
author = {Nino Antulov-Fantulin and Matko Bosnjak and Vinko Zlatic and Miha Grcar and Tomislav Smuc},
journal= {arXiv preprint arXiv:1201.6134},
year = {2015}
}
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