139,670 research outputs found
An Empirical Study of Clarifying Question-Based Systems
Search and recommender systems that take the initiative to ask clarifying
questions to better understand users' information needs are receiving
increasing attention from the research community. However, to the best of our
knowledge, there is no empirical study to quantify whether and to what extent
users are willing or able to answer these questions. In this work, we conduct
an online experiment by deploying an experimental system, which interacts with
users by asking clarifying questions against a product repository. We collect
both implicit interaction behavior data and explicit feedback from users
showing that: (a) users are willing to answer a good number of clarifying
questions (11-21 on average), but not many more than that; (b) most users
answer questions until they reach the target product, but also a fraction of
them stops due to fatigue or due to receiving irrelevant questions; (c) part of
the users' answers (12-17%) are actually opposite to the description of the
target product; while (d) most of the users (66-84%) find the question-based
system helpful towards completing their tasks. Some of the findings of the
study contradict current assumptions on simulated evaluations in the field,
while they point towards improvements in the evaluation framework and can
inspire future interactive search/recommender system designs.Comment: Parts of content are published on CIKM 202
Finding Relevant Answers in Software Forums
Abstract—Online software forums provide a huge amount of valuable content. Developers and users often ask questions and receive answers from such forums. The availability of a vast amount of thread discussions in forums provides ample opportunities for knowledge acquisition and summarization. For a given search query, current search engines use traditional information retrieval approach to extract webpages containin
Applied statistics: A review
The main phases of applied statistical work are discussed in general terms.
The account starts with the clarification of objectives and proceeds through
study design, measurement and analysis to interpretation. An attempt is made to
extract some general notions.Comment: Published at http://dx.doi.org/10.1214/07-AOAS113 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
The view from elsewhere: perspectives on ALife Modeling
Many artificial life researchers stress the interdisciplinary character of the field. Against such a backdrop, this report reviews and discusses artificial life, as it is depicted in, and as it interfaces with, adjacent disciplines (in particular, philosophy, biology, and linguistics), and in the light of a specific historical example of interdisciplinary research (namely cybernetics) with which artificial life shares many features. This report grew out of a workshop held at the Sixth European Conference on Artificial Life in Prague and features individual contributions from the workshop's eight speakers, plus a section designed to reflect the debates that took place during the workshop's discussion sessions. The major theme that emerged during these sessions was the identity and status of artificial life as a scientific endeavor
Lessons from Learning the Craft of Theory-Driven Research
This article presents a case study of the structure and logic of the author’s dissertation, with a focus on theoretical content. Designed for use in proposal writing seminars or research methods courses, the article stresses the value of identifying the originating, specifying and subsidiary research questions; clarifying the subject and object of the research; situating research within a particular research tradition, and using a competing theories approach. The article stresses the need to identify conceptual problems and empirical problems and their associated conceptual and operational definitions. The primary theoretical perspective is drawn from emerging sociology of externalities rooted in ecological theory, within the institutionalist tradition
Mathematical practice, crowdsourcing, and social machines
The highest level of mathematics has traditionally been seen as a solitary
endeavour, to produce a proof for review and acceptance by research peers.
Mathematics is now at a remarkable inflexion point, with new technology
radically extending the power and limits of individuals. Crowdsourcing pulls
together diverse experts to solve problems; symbolic computation tackles huge
routine calculations; and computers check proofs too long and complicated for
humans to comprehend.
Mathematical practice is an emerging interdisciplinary field which draws on
philosophy and social science to understand how mathematics is produced. Online
mathematical activity provides a novel and rich source of data for empirical
investigation of mathematical practice - for example the community question
answering system {\it mathoverflow} contains around 40,000 mathematical
conversations, and {\it polymath} collaborations provide transcripts of the
process of discovering proofs. Our preliminary investigations have demonstrated
the importance of "soft" aspects such as analogy and creativity, alongside
deduction and proof, in the production of mathematics, and have given us new
ways to think about the roles of people and machines in creating new
mathematical knowledge. We discuss further investigation of these resources and
what it might reveal.
Crowdsourced mathematical activity is an example of a "social machine", a new
paradigm, identified by Berners-Lee, for viewing a combination of people and
computers as a single problem-solving entity, and the subject of major
international research endeavours. We outline a future research agenda for
mathematics social machines, a combination of people, computers, and
mathematical archives to create and apply mathematics, with the potential to
change the way people do mathematics, and to transform the reach, pace, and
impact of mathematics research.Comment: To appear, Springer LNCS, Proceedings of Conferences on Intelligent
Computer Mathematics, CICM 2013, July 2013 Bath, U
What drives contract design in strategic alliances? Taking stock and how to proceed
We collect and assess prior empirical evidence on contract design in alliances that has been published since Parkhe’s (1993) seminal study on inter-firm contracts. We elaborate on the effects of transaction-related factors, experience gained from prior relationships, and deliberate learning efforts on contracts. Our paper offers three contributions. First, we systematically review the existing literature on alliance contracts and summarize our findings. Second, while prior research has traditionally focused on contractual complexity, we place the content of contracts center stage and identify three contractual functions. While existing studies on contractual functions predominantly refer to safeguarding as a response to appropriation concerns, we also consider coordination and contingency adaptability as outcomes of adaptation concerns. Third, we disentangle the differential influences of previous experiences on distinct contractual functions and show that experience gained from prior relationships has different effects on safeguarding and contingency adaptability than on coordination. Overall, we add to the systematization of the current debate on alliance contract design and trace promising avenues for future research on the impact of transaction- and experience-related factors on the complexity and content of alliance contracts
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