3,926 research outputs found
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
Social Influences in Recommendation Systems
Social networking sites such as Flickr and Facebook allow users to share
content with family, friends, and interest groups. Also, tags can often assign
to resources. In the previous research using few association rules FAR, we have
seen that high-quality and efficient association-based tag recommendation is
possible, but the set-up that we considered was very generic and did not take
social information into account. The proposed method in the previous paper,
FAR, in particular, exhibited a favorable trade-off between recommendation
quality and runtime. Unfortunately, recommendation quality is unlikely to be
optimal because the algorithms are not aware of any social information that may
be available. Two proposed approaches take a more social view on tag
recommendation regarding the issue: social contact variants and social groups
of interest. The user data is varied and used as a source of associations. The
adoption of social contact variants has two approaches. The first social
variant is User-centered Knowledge, to contrast Collective Knowledge. It
improves tag recommendation by grouping historic tag data according to friend
relationships and interests. The second variant is dubbed 'social batched
personomy' and attempts to address both quality and scalability issues by
processing queries in batches instead of individually, such as done in a
conventional personomy approach. For the social group of interest, 'community
batched personomy' is proposed to provide better accuracy groups of
recommendation systems in contrast also to Collective Knowledge. By taking
social information into account can enhance the performance of recommendation
systems.Comment: 6 page
Social software for music
Tese de mestrado integrado. Engenharia Informática e Computação. Faculdade de Engenharia. Universidade do Porto. 200
The role of social networks in students’ learning experiences
The aim of this research is to investigate the role of social networks in computer science education. The Internet shows great potential for enhancing collaboration between people and the role of social software has become increasingly relevant in recent years. This research focuses on analyzing the role that social networks play in students’ learning experiences. The construction of students’ social networks, the evolution of these networks, and their effects on the students’ learning experience in a university environment are examined
Geotag propagation in social networks based on user trust model
In the past few years sharing photos within social networks has become very popular. In order to make these huge collections easier to explore, images are usually tagged with representative keywords such as persons, events, objects, and locations. In order to speed up the time consuming tag annotation process, tags can be propagated based on the similarity between image content and context. In this paper, we present a system for efficient geotag propagation based on a combination of object duplicate detection and user trust modeling. The geotags are propagated by training a graph based object model for each of the landmarks on a small tagged image set and finding its duplicates within a large untagged image set. Based on the established correspondences between these two image sets and the reliability of the user, tags are propagated from the tagged to the untagged images. The user trust modeling reduces the risk of propagating wrong tags caused by spamming or faulty annotation. The effectiveness of the proposed method is demonstrated through a set of experiments on an image database containing various landmark
Learning in Social Networks: Rationale and Ideas for Its Implementation in Higher Education
The internet has fast become a prevalent medium for collaboration between people and social networks, in particular, have gained vast popularity and relevance over the past few years. Within this framework, our paper will analyse the role played by social networks in current teaching practices. Specifically, we focus on the principles guiding the design of study activities which use social networks and we relate concrete experiences that show how they contribute to improving teaching and learning within a university environment
Review of the state of the art: discovering and associating semantics to tags in folksonomies
This paper describes and compares the most relevant approaches for associating tags with semantics in order to make explicit the meaning of those tags. We identify a common set of steps that are usually considered across all these approaches and frame our descriptions according to them, providing a unified view of how each approach tackles the different problems that appear during the semantic association process. Furthermore, we provide some recommendations on (a) how and when to use each of the approaches according to the characteristics of the data source, and (b) how to improve results by leveraging the strengths of the different approaches
Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval
Where previous reviews on content-based image retrieval emphasize on what can
be seen in an image to bridge the semantic gap, this survey considers what
people tag about an image. A comprehensive treatise of three closely linked
problems, i.e., image tag assignment, refinement, and tag-based image retrieval
is presented. While existing works vary in terms of their targeted tasks and
methodology, they rely on the key functionality of tag relevance, i.e.
estimating the relevance of a specific tag with respect to the visual content
of a given image and its social context. By analyzing what information a
specific method exploits to construct its tag relevance function and how such
information is exploited, this paper introduces a taxonomy to structure the
growing literature, understand the ingredients of the main works, clarify their
connections and difference, and recognize their merits and limitations. For a
head-to-head comparison between the state-of-the-art, a new experimental
protocol is presented, with training sets containing 10k, 100k and 1m images
and an evaluation on three test sets, contributed by various research groups.
Eleven representative works are implemented and evaluated. Putting all this
together, the survey aims to provide an overview of the past and foster
progress for the near future.Comment: to appear in ACM Computing Survey
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