37,505 research outputs found
Design and Development of a User Specific Dynamic E-Magazine
Internet and electronic media gaining more popularity due to ease and speed,
the count of Internet users has increased tremendously. The world is moving
faster each day with several events taking place at once and the Internet is
flooded with information in every field. There are categories of information
ranging from most relevant to user, to the information totally irrelevant or
less relevant to specific users. In such a scenario getting the information
which is most relevant to the user is indispensable to save time. The
motivation of our solution is based on the idea of optimizing the search for
information automatically. This information is delivered to user in the form of
an interactive GUI. The optimization of the contents or information served to
him is based on his social networking profiles and on his reading habits on the
proposed solution. The aim is to get the user's profile information based on
his social networking profile considering that almost every Internet user has
one. This helps us personalize the contents delivered to the user in order to
produce what is most relevant to him, in the form of a personalized e-magazine.
Further the proposed solution learns user's reading habits for example the news
he saves or clicks the most and makes a decision to provide him with the best
contents.Comment: 19 pages, 6 figure
Using Social Media to Promote STEM Education: Matching College Students with Role Models
STEM (Science, Technology, Engineering, and Mathematics) fields have become
increasingly central to U.S. economic competitiveness and growth. The shortage
in the STEM workforce has brought promoting STEM education upfront. The rapid
growth of social media usage provides a unique opportunity to predict users'
real-life identities and interests from online texts and photos. In this paper,
we propose an innovative approach by leveraging social media to promote STEM
education: matching Twitter college student users with diverse LinkedIn STEM
professionals using a ranking algorithm based on the similarities of their
demographics and interests. We share the belief that increasing STEM presence
in the form of introducing career role models who share similar interests and
demographics will inspire students to develop interests in STEM related fields
and emulate their models. Our evaluation on 2,000 real college students
demonstrated the accuracy of our ranking algorithm. We also design a novel
implementation that recommends matched role models to the students.Comment: 16 pages, 8 figures, accepted by ECML/PKDD 2016, Industrial Trac
FARS: Fuzzy Ant based Recommender System for Web Users
Recommender systems are useful tools which provide an
adaptive web environment for web users. Nowadays, having a
user friendly website is a big challenge in e-commerce
technology. In this paper, applying the benefits of both
collaborative and content based filtering techniques is proposed by presenting a fuzzy recommender system based on
collaborative behavior of ants (FARS). FARS works in two
phases: modeling and recommendation. First, user’s behaviors
are modeled offline and the results are used in second phase for online recommendation. Fuzzy techniques provide the possibility of capturing uncertainty among user interests and ant based algorithms provides us with optimal solutions. The performance of FARS is evaluated using log files of “Information and Communication Technology Center” of Isfahan municipality in Iran and compared with ant based recommender system (ARS). The results shown are promising and proved that integrating fuzzy Ant approach provides us with more functional and robust recommendations
Web-Based Knowledge Extraction and the Cognitive Characterization of Cultural Groups
The advent of Web 2.0 has provided new opportunities for cultural analysts to understand more about the cognitive characteristics of cultural groups. In particular, user-contributed content provides important indications as to the beliefs, attitudes and values of cultural groups, and this is an important focus of attention for those concerned with the development of cognitively-relevant models. In order to support the exploitation of the Web in the context of cultural modeling activities, it is important to deal with both the large-scale nature of the Web and the current dominance of natural language formats. In this paper, we outline an approach to support the exploitation of the Web in the context of cultural modeling activities. The approach begins with the development of qualitative cultural models (which describe the beliefs, concepts and values of cultural groups), and these models are subsequently used to develop an ontology-based information extraction capability (which harvests model-relevant information from online textual resources). We are currently developing a system to support the approach, and the continued development of this system should enable cultural analysts to more fully exploit the Web for the purpose of developing more accurate, detailed and predictively-relevant cognitive models
PACMAS: A Personalized, Adaptive, and Cooperative MultiAgent System Architecture
In this paper, a generic architecture, designed to
support the implementation of applications aimed at managing
information among different and heterogeneous sources,
is presented. Information is filtered and organized according
to personal interests explicitly stated by the user. User pro-
files are improved and refined throughout time by suitable
adaptation techniques. The overall architecture has been called
PACMAS, being a support for implementing Personalized, Adaptive,
and Cooperative MultiAgent Systems. PACMAS agents are
autonomous and flexible, and can be made personal, adaptive and
cooperative, depending on the given application. The peculiarities
of the architecture are highlighted by illustrating three relevant
case studies focused on giving a support to undergraduate and
graduate students, on predicting protein secondary structure, and
on classifying newspaper articles, respectively
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