17,761 research outputs found
Toward a model of computational attention based on expressive behavior: applications to cultural heritage scenarios
Our project goals consisted in the development of attention-based analysis of human expressive behavior and the implementation of real-time algorithm in EyesWeb XMI in order to improve naturalness of human-computer interaction and context-based monitoring of human behavior. To this aim, perceptual-model that mimic human attentional processes was developed for expressivity analysis and modeled by entropy. Museum scenarios were selected as an ecological test-bed to elaborate three experiments that focus on visitor profiling and visitors flow regulation
The contribution of data mining to information science
The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
Social Media for Cities, Counties and Communities
Social media (i.e., Twitter, Facebook, Flickr, YouTube) and other tools and services with user- generated content have made a staggering amount of information (and misinformation) available. Some government officials seek to leverage these resources to improve services and communication with citizens, especially during crises and emergencies. Yet, the sheer volume of social data streams generates substantial noise that must be filtered. Potential exists to rapidly identify issues of concern for emergency management by detecting meaningful patterns or trends in the stream of messages and information flow. Similarly, monitoring these patterns and themes over time could provide officials with insights into the perceptions and mood of the community that cannot be collected through traditional methods (e.g., phone or mail surveys) due to their substantive costs, especially in light of reduced and shrinking budgets of governments at all levels. We conducted a pilot study in 2010 with government officials in Arlington, Virginia (and to a lesser extent representatives of groups from Alexandria and Fairfax, Virginia) with a view to contributing to a general understanding of the use of social media by government officials as well as community organizations, businesses and the public. We were especially interested in gaining greater insight into social media use in crisis situations (whether severe or fairly routine crises, such as traffic or weather disruptions)
Co-Following on Twitter
We present an in-depth study of co-following on Twitter based on the
observation that two Twitter users whose followers have similar friends are
also similar, even though they might not share any direct links or a single
mutual follower. We show how this observation contributes to (i) a better
understanding of language-agnostic user classification on Twitter, (ii)
eliciting opportunities for Computational Social Science, and (iii) improving
online marketing by identifying cross-selling opportunities.
We start with a machine learning problem of predicting a user's preference
among two alternative choices of Twitter friends. We show that co-following
information provides strong signals for diverse classification tasks and that
these signals persist even when (i) the most discriminative features are
removed and (ii) only relatively "sparse" users with fewer than 152 but more
than 43 Twitter friends are considered.
Going beyond mere classification performance optimization, we present
applications of our methodology to Computational Social Science. Here we
confirm stereotypes such as that the country singer Kenny Chesney
(@kennychesney) is more popular among @GOP followers, whereas Lady Gaga
(@ladygaga) enjoys more support from @TheDemocrats followers.
In the domain of marketing we give evidence that celebrity endorsement is
reflected in co-following and we demonstrate how our methodology can be used to
reveal the audience similarities between Apple and Puma and, less obviously,
between Nike and Coca-Cola. Concerning a user's popularity we find a
statistically significant connection between having a more "average"
followership and having more followers than direct rivals. Interestingly, a
\emph{larger} audience also seems to be linked to a \emph{less diverse}
audience in terms of their co-following.Comment: full version of a short paper at Hypertext 201
News Session-Based Recommendations using Deep Neural Networks
News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods.Comment: Accepted for the Third Workshop on Deep Learning for Recommender
Systems - DLRS 2018, October 02-07, 2018, Vancouver, Canada.
https://recsys.acm.org/recsys18/dlrs
Analyzing Tag Semantics Across Collaborative Tagging Systems
The objective of our group was to exploit state-of-the-art Information Retrieval methods for finding associations and dependencies between tags, capturing and representing differences in tagging behavior and vocabulary of various folksonomies, with the overall aim to better understand the semantics of tags and the tagging process. Therefore we analyze the semantic content of tags in the Flickr and Delicious folksonomies. We find that: tag context similarity leads to meaningful results in Flickr, despite its narrow folksonomy character; the comparison of tags across Flickr and Delicious shows little semantic overlap, being tags in Flickr associated more to visual aspects rather than technological as it seems to be in Delicious; there are regions in the tag-tag space, provided with the cosine similarity metric, that are characterized by high density; the order of tags inside a post has a semantic relevance
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