4,505 research outputs found
Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media
Most of the online news media outlets rely heavily on the revenues generated
from the clicks made by their readers, and due to the presence of numerous such
outlets, they need to compete with each other for reader attention. To attract
the readers to click on an article and subsequently visit the media site, the
outlets often come up with catchy headlines accompanying the article links,
which lure the readers to click on the link. Such headlines are known as
Clickbaits. While these baits may trick the readers into clicking, in the long
run, clickbaits usually don't live up to the expectation of the readers, and
leave them disappointed.
In this work, we attempt to automatically detect clickbaits and then build a
browser extension which warns the readers of different media sites about the
possibility of being baited by such headlines. The extension also offers each
reader an option to block clickbaits she doesn't want to see. Then, using such
reader choices, the extension automatically blocks similar clickbaits during
her future visits. We run extensive offline and online experiments across
multiple media sites and find that the proposed clickbait detection and the
personalized blocking approaches perform very well achieving 93% accuracy in
detecting and 89% accuracy in blocking clickbaits.Comment: 2016 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining (ASONAM
Video summarisation: A conceptual framework and survey of the state of the art
This is the post-print (final draft post-refereeing) version of the article. Copyright @ 2007 Elsevier Inc.Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. This paper presents a conceptual framework for video summarisation derived from the research literature and used as a means for surveying the research literature. The framework distinguishes between video summarisation techniques (the methods used to process content from a source video stream to achieve a summarisation of that stream) and video summaries (outputs of video summarisation techniques). Video summarisation techniques are considered within three broad categories: internal (analyse information sourced directly from the video stream), external (analyse information not sourced directly from the video stream) and hybrid (analyse a combination of internal and external information). Video summaries are considered as a function of the type of content they are derived from (object, event, perception or feature based) and the functionality offered to the user for their consumption (interactive or static, personalised or generic). It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users
Signed Distance-based Deep Memory Recommender
Personalized recommendation algorithms learn a user's preference for an item
by measuring a distance/similarity between them. However, some of the existing
recommendation models (e.g., matrix factorization) assume a linear relationship
between the user and item. This approach limits the capacity of recommender
systems, since the interactions between users and items in real-world
applications are much more complex than the linear relationship. To overcome
this limitation, in this paper, we design and propose a deep learning framework
called Signed Distance-based Deep Memory Recommender, which captures non-linear
relationships between users and items explicitly and implicitly, and work well
in both general recommendation task and shopping basket-based recommendation
task. Through an extensive empirical study on six real-world datasets in the
two recommendation tasks, our proposed approach achieved significant
improvement over ten state-of-the-art recommendation models
Survey of data mining approaches to user modeling for adaptive hypermedia
The ability of an adaptive hypermedia system to create tailored environments depends mainly on the amount and accuracy of information stored in each user model. Some of the difficulties that user modeling faces are the amount of data available to create user models, the adequacy of the data, the noise within that data, and the necessity of capturing the imprecise nature of human behavior. Data mining and machine learning techniques have the ability to handle large amounts of data and to process uncertainty. These characteristics make these techniques suitable for automatic generation of user models that simulate human decision making. This paper surveys different data mining techniques that can be used to efficiently and accurately capture user behavior. The paper also presents guidelines that show which techniques may be used more efficiently according to the task implemented by the applicatio
Recommender Systems for Online and Mobile Social Networks: A survey
Recommender Systems (RS) currently represent a fundamental tool in online
services, especially with the advent of Online Social Networks (OSN). In this
case, users generate huge amounts of contents and they can be quickly
overloaded by useless information. At the same time, social media represent an
important source of information to characterize contents and users' interests.
RS can exploit this information to further personalize suggestions and improve
the recommendation process. In this paper we present a survey of Recommender
Systems designed and implemented for Online and Mobile Social Networks,
highlighting how the use of social context information improves the
recommendation task, and how standard algorithms must be enhanced and optimized
to run in a fully distributed environment, as opportunistic networks. We
describe advantages and drawbacks of these systems in terms of algorithms,
target domains, evaluation metrics and performance evaluations. Eventually, we
present some open research challenges in this area
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