2,596 research outputs found
Recommender Systems
The ongoing rapid expansion of the Internet greatly increases the necessity
of effective recommender systems for filtering the abundant information.
Extensive research for recommender systems is conducted by a broad range of
communities including social and computer scientists, physicists, and
interdisciplinary researchers. Despite substantial theoretical and practical
achievements, unification and comparison of different approaches are lacking,
which impedes further advances. In this article, we review recent developments
in recommender systems and discuss the major challenges. We compare and
evaluate available algorithms and examine their roles in the future
developments. In addition to algorithms, physical aspects are described to
illustrate macroscopic behavior of recommender systems. Potential impacts and
future directions are discussed. We emphasize that recommendation has a great
scientific depth and combines diverse research fields which makes it of
interests for physicists as well as interdisciplinary researchers.Comment: 97 pages, 20 figures (To appear in Physics Reports
A General Framework for Complex Network Applications
Complex network theory has been applied to solving practical problems from
different domains. In this paper, we present a general framework for complex
network applications. The keys of a successful application are a thorough
understanding of the real system and a correct mapping of complex network
theory to practical problems in the system. Despite of certain limitations
discussed in this paper, complex network theory provides a foundation on which
to develop powerful tools in analyzing and optimizing large interconnected
systems.Comment: 8 page
The Emerging Scholarly Brain
It is now a commonplace observation that human society is becoming a coherent
super-organism, and that the information infrastructure forms its emerging
brain. Perhaps, as the underlying technologies are likely to become billions of
times more powerful than those we have today, we could say that we are now
building the lizard brain for the future organism.Comment: to appear in Future Professional Communication in Astronomy-II
(FPCA-II) editors A. Heck and A. Accomazz
Term-community-based topic detection with variable resolution
Network-based procedures for topic detection in huge text collections offer
an intuitive alternative to probabilistic topic models. We present in detail a
method that is especially designed with the requirements of domain experts in
mind. Like similar methods, it employs community detection in term
co-occurrence graphs, but it is enhanced by including a resolution parameter
that can be used for changing the targeted topic granularity. We also establish
a term ranking and use semantic word-embedding for presenting term communities
in a way that facilitates their interpretation. We demonstrate the application
of our method with a widely used corpus of general news articles and show the
results of detailed social-sciences expert evaluations of detected topics at
various resolutions. A comparison with topics detected by Latent Dirichlet
Allocation is also included. Finally, we discuss factors that influence topic
interpretation.Comment: 31 pages, 6 figure
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