29,251 research outputs found
Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study
Recommender systems engage user profiles and appropriate filtering techniques
to assist users in finding more relevant information over the large volume of
information. User profiles play an important role in the success of
recommendation process since they model and represent the actual user needs.
However, a comprehensive literature review of recommender systems has
demonstrated no concrete study on the role and impact of knowledge in user
profiling and filtering approache. In this paper, we review the most prominent
recommender systems in the literature and examine the impression of knowledge
extracted from different sources. We then come up with this finding that
semantic information from the user context has substantial impact on the
performance of knowledge based recommender systems. Finally, some new clues for
improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science &
Engineering Survey (IJCSES) Vol.2, No.3, August 201
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information
Metadata are associated to most of the information we produce in our daily
interactions and communication in the digital world. Yet, surprisingly,
metadata are often still catergorized as non-sensitive. Indeed, in the past,
researchers and practitioners have mainly focused on the problem of the
identification of a user from the content of a message.
In this paper, we use Twitter as a case study to quantify the uniqueness of
the association between metadata and user identity and to understand the
effectiveness of potential obfuscation strategies. More specifically, we
analyze atomic fields in the metadata and systematically combine them in an
effort to classify new tweets as belonging to an account using different
machine learning algorithms of increasing complexity. We demonstrate that
through the application of a supervised learning algorithm, we are able to
identify any user in a group of 10,000 with approximately 96.7% accuracy.
Moreover, if we broaden the scope of our search and consider the 10 most likely
candidates we increase the accuracy of the model to 99.22%. We also found that
data obfuscation is hard and ineffective for this type of data: even after
perturbing 60% of the training data, it is still possible to classify users
with an accuracy higher than 95%. These results have strong implications in
terms of the design of metadata obfuscation strategies, for example for data
set release, not only for Twitter, but, more generally, for most social media
platforms.Comment: 11 pages, 13 figures. Published in the Proceedings of the 12th
International AAAI Conference on Web and Social Media (ICWSM 2018). June
2018. Stanford, CA, US
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