338 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
A principal component analysis of 39 scientific impact measures
The impact of scientific publications has traditionally been expressed in
terms of citation counts. However, scientific activity has moved online over
the past decade. To better capture scientific impact in the digital era, a
variety of new impact measures has been proposed on the basis of social network
analysis and usage log data. Here we investigate how these new measures relate
to each other, and how accurately and completely they express scientific
impact. We performed a principal component analysis of the rankings produced by
39 existing and proposed measures of scholarly impact that were calculated on
the basis of both citation and usage log data. Our results indicate that the
notion of scientific impact is a multi-dimensional construct that can not be
adequately measured by any single indicator, although some measures are more
suitable than others. The commonly used citation Impact Factor is not
positioned at the core of this construct, but at its periphery, and should thus
be used with caution
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