64,843 research outputs found
Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora
Much of scientific progress stems from previously published findings, but
searching through the vast sea of scientific publications is difficult. We
often rely on metrics of scholarly authority to find the prominent authors but
these authority indices do not differentiate authority based on research
topics. We present Latent Topical-Authority Indexing (LTAI) for jointly
modeling the topics, citations, and topical authority in a corpus of academic
papers. Compared to previous models, LTAI differs in two main aspects. First,
it explicitly models the generative process of the citations, rather than
treating the citations as given. Second, it models each author's influence on
citations of a paper based on the topics of the cited papers, as well as the
citing papers. We fit LTAI to four academic corpora: CORA, Arxiv Physics, PNAS,
and Citeseer. We compare the performance of LTAI against various baselines,
starting with the latent Dirichlet allocation, to the more advanced models
including author-link topic model and dynamic author citation topic model. The
results show that LTAI achieves improved accuracy over other similar models
when predicting words, citations and authors of publications.Comment: Accepted by Transactions of the Association for Computational
Linguistics (TACL); to appea
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
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The Double-Edged Sword of Industry Collaboration: Evidence from Engineering Academics in the UK
This paper studies the impact of university-industry collaboration on academic research output. We report findings from a unique longitudinal dataset on all the researchers in all the engineering departments of 40 major universities in the UK for the last 20 years. We introduce a new measure of industry collaboration based on the fraction of research grants that include industry partners. Our results show that productivity increases with the intensity of industry collaboration, but only up to a certain point. Above a certain threshold, research productivity declines. Our results are robust to several econometric estimation methods, measures of research output, and for various subsamples of academics
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