30 research outputs found
Scholarly event characteristics in four fields of science : a metrics-based analysis
One of the key channels of scholarly knowledge exchange are scholarly events such as conferences, workshops, symposiums, etc.; such events are especially important and popular in Computer Science, Engineering, and Natural Sciences.However, scholars encounter problems in finding relevant information about upcoming events and statistics on their historic evolution.In order to obtain a better understanding of scholarly event characteristics in four fields of science, we analyzed the metadata of scholarly events of four major fields of science, namely Computer Science, Physics, Engineering, and Mathematics using Scholarly Events Quality Assessment suite, a suite of ten metrics.In particular, we analyzed renowned scholarly events belonging to five sub-fields within Computer Science, namely World Wide Web, Computer Vision, Software Engineering, Data Management, as well as Security and Privacy.This analysis is based on a systematic approach using descriptive statistics as well as exploratory data analysis. The findings are on the one hand interesting to observe the general evolution and success factors of scholarly events; on the other hand, they allow (prospective) event organizers, publishers, and committee members to assess the progress of their event over time and compare it to other events in the same field; and finally, they help researchers to make more informed decisions when selecting suitable venues for presenting their work.Based on these findings, a set of recommendations has been concluded to different stakeholders, involving event organizers, potential authors, proceedings publishers, and sponsors. Our comprehensive dataset of scholarly events of the aforementioned fields is openly available in a semantic format and maintained collaboratively at OpenResearch.org. © 2020, The Author(s)
Information Outlook, January 2008
Volume 12, Issue 1https://scholarworks.sjsu.edu/sla_io_2008/1000/thumbnail.jp
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Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: NL
Proceedings ICPW'07: 2nd International Conference on the Pragmatic Web, 22-23 Oct. 2007, Tilburg: N
Personalized Expert Recommendation: Models and Algorithms
Many large-scale information sharing systems including social media systems, questionanswering
sites and rating and reviewing applications have been growing rapidly, allowing
millions of human participants to generate and consume information on an unprecedented
scale. To manage the sheer growth of information generation, there comes the need to enable
personalization of information resources for users — to surface high-quality content
and feeds, to provide personally relevant suggestions, and so on. A fundamental task in
creating and supporting user-centered personalization systems is to build rich user profile
to aid recommendation for better user experience.
Therefore, in this dissertation research, we propose models and algorithms to facilitate
the creation of new crowd-powered personalized information sharing systems. Specifically,
we first give a principled framework to enable personalization of resources so that
information seekers can be matched with customized knowledgeable users based on their
previous historical actions and contextual information; We then focus on creating rich
user models that allows accurate and comprehensive modeling of user profiles for long
tail users, including discovering user’s known-for profile, user’s opinion bias and user’s
geo-topic profile. In particular, this dissertation research makes two unique contributions:
First, we introduce the problem of personalized expert recommendation and propose
the first principled framework for addressing this problem. To overcome the sparsity issue,
we investigate the use of user’s contextual information that can be exploited to build robust
models of personal expertise, study how spatial preference for personally-valuable expertise
varies across regions, across topics and based on different underlying social communities,
and integrate these different forms of preferences into a matrix factorization-based
personalized expert recommender.
Second, to support the personalized recommendation on experts, we focus on modeling
and inferring user profiles in online information sharing systems. In order to tap
the knowledge of most majority of users, we provide frameworks and algorithms to accurately
and comprehensively create user models by discovering user’s known-for profile,
user’s opinion bias and user’s geo-topic profile, with each described shortly as follows:
—We develop a probabilistic model called Bayesian Contextual Poisson Factorization
to discover what users are known for by others. Our model considers as input a small fraction
of users whose known-for profiles are already known and the vast majority of users for
whom we have little (or no) information, learns the implicit relationships between user?s
known-for profiles and their contextual signals, and finally predict known-for profiles for
those majority of users.
—We explore user’s topic-sensitive opinion bias, propose a lightweight semi-supervised
system called “BiasWatch” to semi-automatically infer the opinion bias of long-tail users,
and demonstrate how user’s opinion bias can be exploited to recommend other users with
similar opinion in social networks.
— We study how a user’s topical profile varies geo-spatially and how we can model
a user’s geo-spatial known-for profile as the last step in our dissertation for creation of
rich user profile. We propose a multi-layered Bayesian hierarchical user factorization to
overcome user heterogeneity and an enhanced model to alleviate the sparsity issue by integrating
user contexts into the two-layered hierarchical user model for better representation
of user’s geo-topic preference by others
Multimedia interaction and access based on emotions:automating video elicited emotions recognition and visualization
Tese de doutoramento, Informática (Engenharia Informática), Universidade de Lisboa, Faculdade de Ciências, 2013Films are an excellent form of art that exploit our affective, perceptual and intellectual
abilities. Technological developments and the trends for media convergence are turning
video into a dominant and pervasive medium, and online video is becoming a growing
entertainment activity on the web. Alongside, physiological measures are making it
possible to study additional ways to identify and use emotions in human-machine
interactions, multimedia retrieval and information visualization.
The work described in this thesis has two main objectives: to develop an Emotions
Recognition and Classification mechanism for video induced emotions; and to enable
Emotional Movie Access and Exploration. Regarding the first objective, we explore
recognition and classification mechanisms, in order to allow video classification based
on emotions, and to identify each user’s emotional states providing different access
mechanisms. We aim to provide video classification and indexing based on emotions,
felt by the users while watching movies. In what concerns the second objective, we
focus on emotional movie access and exploration mechanisms to find ways to access
and visualize videos based on their emotional properties and users’ emotions and
profiles. In this context, we designed a set of methods to access and watch the movies,
both at the level of the whole movie collection, and at the individual movies level.
The automatic recognition mechanism developed in this work allows for the detection
of physiologic patterns, indeed providing valid individual information about users
emotion while they were watching a specific movie; in addition, the user interface
representations and exploration mechanisms proposed and evaluated in this thesis, show
that more perceptive, satisfactory and useful visual representations influenced positively
the exploration of emotional information in movies.Fundação para a Ciência e a Tecnologia (FCT, PROTEC SFRH/BD/49475/2009, LASIGE Multiannual Funding e VIRUS projecto (PTDC/EIAEIA/101012/2008
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B!SON: A Tool for Open Access Journal Recommendation
Finding a suitable open access journal to publish scientific work is a complex task: Researchers have to navigate a constantly growing number of journals, institutional agreements with publishers, funders’ conditions and the risk of Predatory Publishers. To help with these challenges, we introduce a web-based journal recommendation system called B!SON. It is developed based on a systematic requirements analysis, built on open data, gives publisher-independent recommendations and works across domains. It suggests open access journals based on title, abstract and references provided by the user. The recommendation quality has been evaluated using a large test set of 10,000 articles. Development by two German scientific libraries ensures the longevity of the project
Personalized Expert Recommendation: Models and Algorithms
Many large-scale information sharing systems including social media systems, questionanswering
sites and rating and reviewing applications have been growing rapidly, allowing
millions of human participants to generate and consume information on an unprecedented
scale. To manage the sheer growth of information generation, there comes the need to enable
personalization of information resources for users — to surface high-quality content
and feeds, to provide personally relevant suggestions, and so on. A fundamental task in
creating and supporting user-centered personalization systems is to build rich user profile
to aid recommendation for better user experience.
Therefore, in this dissertation research, we propose models and algorithms to facilitate
the creation of new crowd-powered personalized information sharing systems. Specifically,
we first give a principled framework to enable personalization of resources so that
information seekers can be matched with customized knowledgeable users based on their
previous historical actions and contextual information; We then focus on creating rich
user models that allows accurate and comprehensive modeling of user profiles for long
tail users, including discovering user’s known-for profile, user’s opinion bias and user’s
geo-topic profile. In particular, this dissertation research makes two unique contributions:
First, we introduce the problem of personalized expert recommendation and propose
the first principled framework for addressing this problem. To overcome the sparsity issue,
we investigate the use of user’s contextual information that can be exploited to build robust
models of personal expertise, study how spatial preference for personally-valuable expertise
varies across regions, across topics and based on different underlying social communities,
and integrate these different forms of preferences into a matrix factorization-based
personalized expert recommender.
Second, to support the personalized recommendation on experts, we focus on modeling
and inferring user profiles in online information sharing systems. In order to tap
the knowledge of most majority of users, we provide frameworks and algorithms to accurately
and comprehensively create user models by discovering user’s known-for profile,
user’s opinion bias and user’s geo-topic profile, with each described shortly as follows:
—We develop a probabilistic model called Bayesian Contextual Poisson Factorization
to discover what users are known for by others. Our model considers as input a small fraction
of users whose known-for profiles are already known and the vast majority of users for
whom we have little (or no) information, learns the implicit relationships between user?s
known-for profiles and their contextual signals, and finally predict known-for profiles for
those majority of users.
—We explore user’s topic-sensitive opinion bias, propose a lightweight semi-supervised
system called “BiasWatch” to semi-automatically infer the opinion bias of long-tail users,
and demonstrate how user’s opinion bias can be exploited to recommend other users with
similar opinion in social networks.
— We study how a user’s topical profile varies geo-spatially and how we can model
a user’s geo-spatial known-for profile as the last step in our dissertation for creation of
rich user profile. We propose a multi-layered Bayesian hierarchical user factorization to
overcome user heterogeneity and an enhanced model to alleviate the sparsity issue by integrating
user contexts into the two-layered hierarchical user model for better representation
of user’s geo-topic preference by others
Processing Structured Hypermedia - A Matter of Style
Vliet, J.C. van [Promotor]Eliens, A. [Copromotor
Recent Advances in Social Data and Artificial Intelligence 2019
The importance and usefulness of subjects and topics involving social data and artificial intelligence are becoming widely recognized. This book contains invited review, expository, and original research articles dealing with, and presenting state-of-the-art accounts pf, the recent advances in the subjects of social data and artificial intelligence, and potentially their links to Cyberspace