36,198 research outputs found
Capturing the Visitor Profile for a Personalized Mobile Museum Experience: an Indirect Approach
An increasing number of museums and cultural institutions
around the world use personalized, mostly mobile, museum
guides to enhance visitor experiences. However since a typical
museum visit may last a few minutes and visitors might only visit
once, the personalization processes need to be quick and efficient,
ensuring the engagement of the visitor. In this paper we
investigate the use of indirect profiling methods through a visitor
quiz, in order to provide the visitor with specific museum content.
Building on our experience of a first study aimed at the design,
implementation and user testing of a short quiz version at the
Acropolis Museum, a second parallel study was devised. This
paper introduces this research, which collected and analyzed data
from two environments: the Acropolis Museum and social media
(i.e. Facebook). Key profiling issues are identified, results are
presented, and guidelines towards a generalized approach for the
profiling needs of cultural institutions are discussed
Contextualised Browsing in a Digital Library's Living Lab
Contextualisation has proven to be effective in tailoring \linebreak search
results towards the users' information need. While this is true for a basic
query search, the usage of contextual session information during exploratory
search especially on the level of browsing has so far been underexposed in
research. In this paper, we present two approaches that contextualise browsing
on the level of structured metadata in a Digital Library (DL), (1) one variant
bases on document similarity and (2) one variant utilises implicit session
information, such as queries and different document metadata encountered during
the session of a users. We evaluate our approaches in a living lab environment
using a DL in the social sciences and compare our contextualisation approaches
against a non-contextualised approach. For a period of more than three months
we analysed 47,444 unique retrieval sessions that contain search activities on
the level of browsing. Our results show that a contextualisation of browsing
significantly outperforms our baseline in terms of the position of the first
clicked item in the result set. The mean rank of the first clicked document
(measured as mean first relevant - MFR) was 4.52 using a non-contextualised
ranking compared to 3.04 when re-ranking the result lists based on similarity
to the previously viewed document. Furthermore, we observed that both
contextual approaches show a noticeably higher click-through rate. A
contextualisation based on document similarity leads to almost twice as many
document views compared to the non-contextualised ranking.Comment: 10 pages, 2 figures, paper accepted at JCDL 201
Aligning the topic of FCA with existing module learning outcomes
Although Formal Concept Analysis is worthy of study on
computing courses, it is not always possible or practical to dedicate a whole module to it. It may, however, fit into an existing module as a topic but require some careful design of teaching and assessment activities to properly align it to the intended learning outcomes of the module. This paper describes and evaluates a three year project to align the teaching and assessment of FCA with the learning outcomes of a final-year undergraduate Smart Applications module at Sheffield Hallam University. Biggs' constructive alignment was used, incorporating an adapted version of Yin's case study research method, in an iterative
process; progressively modifying teaching and assessment activities to align them more closely with the prescribed learning outcomes. The process involved examining conclusions made by students, from carrying out
FCA case study assignments, to draw cross-case conclusions about the learning outcomes achieved, and how they deviated from the prescribed ones. These cross-case conclusions were used to feed back into the design of learning and assessment activities for the next delivery of the module.
After three cycles, the learning outcomes achieved closely matched the prescribed learning outcomes of the module
Automatic domain ontology extraction for context-sensitive opinion mining
Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline
AUC Optimisation and Collaborative Filtering
In recommendation systems, one is interested in the ranking of the predicted
items as opposed to other losses such as the mean squared error. Although a
variety of ways to evaluate rankings exist in the literature, here we focus on
the Area Under the ROC Curve (AUC) as it widely used and has a strong
theoretical underpinning. In practical recommendation, only items at the top of
the ranked list are presented to the users. With this in mind, we propose a
class of objective functions over matrix factorisations which primarily
represent a smooth surrogate for the real AUC, and in a special case we show
how to prioritise the top of the list. The objectives are differentiable and
optimised through a carefully designed stochastic gradient-descent-based
algorithm which scales linearly with the size of the data. In the special case
of square loss we show how to improve computational complexity by leveraging
previously computed measures. To understand theoretically the underlying matrix
factorisation approaches we study both the consistency of the loss functions
with respect to AUC, and generalisation using Rademacher theory. The resulting
generalisation analysis gives strong motivation for the optimisation under
study. Finally, we provide computation results as to the efficacy of the
proposed method using synthetic and real data
Design implications for task-specific search utilities for retrieval and re-engineering of code
The importance of information retrieval systems is unquestionable in the modern society and both individuals as well as enterprises recognise the benefits of being able to find information effectively. Current code-focused information retrieval systems such as Google Code Search, Codeplex or Koders produce results based on specific keywords. However, these systems do not take into account developers’ context such as development language, technology framework, goal of the project, project complexity and developer’s domain expertise. They also impose additional cognitive burden on users in switching between different interfaces and clicking through to find the relevant code. Hence, they are not used by software developers. In this paper, we discuss how software engineers interact with information and general-purpose information retrieval systems (e.g. Google, Yahoo!) and investigate to what extent domain-specific search and recommendation utilities can be developed in order to support their work-related activities. In order to investigate this, we conducted a user study and found that software engineers followed many identifiable and repeatable work tasks and behaviours. These behaviours can be used to develop implicit relevance feedback-based systems based on the observed retention actions. Moreover, we discuss the implications for the development of task-specific search and collaborative recommendation utilities embedded with the Google standard search engine and Microsoft IntelliSense for retrieval and re-engineering of code. Based on implicit relevance feedback, we have implemented a prototype of the proposed collaborative recommendation system, which was evaluated in a controlled environment simulating the real-world situation of professional software engineers. The evaluation has achieved promising initial results on the precision and recall performance of the system
A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web
Over the past decade, rapid advances in web technologies, coupled with
innovative models of spatial data collection and consumption, have generated a
robust growth in geo-referenced information, resulting in spatial information
overload. Increasing 'geographic intelligence' in traditional text-based
information retrieval has become a prominent approach to respond to this issue
and to fulfill users' spatial information needs. Numerous efforts in the
Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the
Linking Open Data initiative have converged in a constellation of open
knowledge bases, freely available online. In this article, we survey these open
knowledge bases, focusing on their geospatial dimension. Particular attention
is devoted to the crucial issue of the quality of geo-knowledge bases, as well
as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic
Network, is outlined as our contribution to this area. Research directions in
information integration and Geographic Information Retrieval (GIR) are then
reviewed, with a critical discussion of their current limitations and future
prospects
A Proximity Indicator for e-Government: The Smallest Number of Clicks
In order to develop an indicator measuring the proximity of e-Government and its different generic functions, we analysed a set of studies that were conducted in the United States and in Europe. We defined 21 elements of measure grouped in six dimensions of proximity and we surveyed the official Websites of the French-speaking Swiss Cantons in 2002 and 2003. We observed that more technical aspects such as navigability were well developed, whereas more “socio-political” aspects (data protection, access for handicapped) and organisational issues were still in early stages. To conclude this work we give some hints for the application of a methodology based on proximity measurement.e-Government; portals; evaluation; proximity; 3-clicks rule; usability
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