7,288 research outputs found
Using Search Engine Technology to Improve Library Catalogs
This chapter outlines how search engine technology can be used in online public access library
catalogs (OPACs) to help improve users’ experiences, to identify users’ intentions, and to indicate
how it can be applied in the library context, along with how sophisticated ranking criteria can be
applied to the online library catalog. A review of the literature and current OPAC developments
form the basis of recommendations on how to improve OPACs. Findings were that the major
shortcomings of current OPACs are that they are not sufficiently user-centered and that their results
presentations lack sophistication. Further, these shortcomings are not addressed in current 2.0
developments. It is argued that OPAC development should be made search-centered before
additional features are applied. While the recommendations on ranking functionality and the use of
user intentions are only conceptual and not yet applied to a library catalogue, practitioners will find
recommendations for developing better OPACs in this chapter. In short, readers will find a
systematic view on how the search engines’ strengths can be applied to improving libraries’ online
catalogs
An integrated ranking algorithm for efficient information computing in social networks
Social networks have ensured the expanding disproportion between the face of
WWW stored traditionally in search engine repositories and the actual ever
changing face of Web. Exponential growth of web users and the ease with which
they can upload contents on web highlights the need of content controls on
material published on the web. As definition of search is changing,
socially-enhanced interactive search methodologies are the need of the hour.
Ranking is pivotal for efficient web search as the search performance mainly
depends upon the ranking results. In this paper new integrated ranking model
based on fused rank of web object based on popularity factor earned over only
valid interlinks from multiple social forums is proposed. This model identifies
relationships between web objects in separate social networks based on the
object inheritance graph. Experimental study indicates the effectiveness of
proposed Fusion based ranking algorithm in terms of better search results.Comment: 14 pages, International Journal on Web Service Computing (IJWSC),
Vol.3, No.1, March 201
Classifying document types to enhance search and recommendations in digital libraries
In this paper, we address the problem of classifying documents available from
the global network of (open access) repositories according to their type. We
show that the metadata provided by repositories enabling us to distinguish
research papers, thesis and slides are missing in over 60% of cases. While
these metadata describing document types are useful in a variety of scenarios
ranging from research analytics to improving search and recommender (SR)
systems, this problem has not yet been sufficiently addressed in the context of
the repositories infrastructure. We have developed a new approach for
classifying document types using supervised machine learning based exclusively
on text specific features. We achieve 0.96 F1-score using the random forest and
Adaboost classifiers, which are the best performing models on our data. By
analysing the SR system logs of the CORE [1] digital library aggregator, we
show that users are an order of magnitude more likely to click on research
papers and thesis than on slides. This suggests that using document types as a
feature for ranking/filtering SR results in digital libraries has the potential
to improve user experience.Comment: 12 pages, 21st International Conference on Theory and Practise of
Digital Libraries (TPDL), 2017, Thessaloniki, Greec
Weathering the Storm of Business Climate Rankings
This paper explores current usage of the term business climate. It discusses multiple definitions and measures of what constitutes a region's business climate. It also examines how usage of the term varies over time and geography. Finally, it addresses some issues related to business climate rankings, their role in economic development policy, and whether the multiplicity of published state rankings contribute consensus or confusion to the meaning of the term business climate.
Biases in scholarly recommender systems: impact, prevalence, and mitigation
We create a simulated financial market and examine the effect of different levels of active and passive investment on fundamental market efficiency. In our simulated market, active, passive, and random investors interact with each other through issuing orders. Active and passive investors select their portfolio weights by optimizing Markowitz-based utility functions. We find that higher fractions of active investment within a market lead to an increased fundamental market efficiency. The marginal increase in fundamental market efficiency per additional active investor is lower in markets with higher levels of active investment. Furthermore, we find that a large fraction of passive investors within a market may facilitate technical price bubbles, resulting in market failure. By examining the effect of specific parameters on market outcomes, we find that that lower transaction costs, lower individual forecasting errors of active investors, and less restrictive portfolio constraints tend to increase fundamental market efficiency in the market
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