602 research outputs found
Effective Retrieval of Resources in Folksonomies Using a New Tag Similarity Measure
Social (or folksonomic) tagging has become a very popular way to describe
content within Web 2.0 websites. However, as tags are informally defined,
continually changing, and ungoverned, it has often been criticised for
lowering, rather than increasing, the efficiency of searching. To address this
issue, a variety of approaches have been proposed that recommend users what
tags to use, both when labeling and when looking for resources. These
techniques work well in dense folksonomies, but they fail to do so when tag
usage exhibits a power law distribution, as it often happens in real-life
folksonomies. To tackle this issue, we propose an approach that induces the
creation of a dense folksonomy, in a fully automatic and transparent way: when
users label resources, an innovative tag similarity metric is deployed, so to
enrich the chosen tag set with related tags already present in the folksonomy.
The proposed metric, which represents the core of our approach, is based on the
mutual reinforcement principle. Our experimental evaluation proves that the
accuracy and coverage of searches guaranteed by our metric are higher than
those achieved by applying classical metrics.Comment: 6 pages, 2 figures, CIKM 2011: 20th ACM Conference on Information and
Knowledge Managemen
Measuring Similarity in Large-Scale Folksonomies
Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. Unlike\ud
taxonomies, which overimpose a hierarchical categorisation of content, folksonomies enable end-users to freely create and choose the categories (in this case, tags) that best\ud
describe some content. However, as tags are informally de-\ud
fined, continually changing, and ungoverned, social tagging\ud
has often been criticised for lowering, rather than increasing, the efficiency of searching, due to the number of synonyms, homonyms, polysemy, as well as the heterogeneity of\ud
users and the noise they introduce. To address this issue, a\ud
variety of approaches have been proposed that recommend\ud
users what tags to use, both when labelling and when looking for resources. As we illustrate in this paper, real world\ud
folksonomies are characterized by power law distributions\ud
of tags, over which commonly used similarity metrics, including the Jaccard coefficient and the cosine similarity, fail\ud
to compute. We thus propose a novel metric, specifically\ud
developed to capture similarity in large-scale folksonomies,\ud
that is based on a mutual reinforcement principle: that is,\ud
two tags are deemed similar if they have been associated to\ud
similar resources, and vice-versa two resources are deemed\ud
similar if they have been labelled by similar tags. We offer an efficient realisation of this similarity metric, and assess its quality experimentally, by comparing it against cosine similarity, on three large-scale datasets, namely Bibsonomy, MovieLens and CiteULike
Tag-Aware Recommender Systems: A State-of-the-art Survey
In the past decade, Social Tagging Systems have attracted increasing
attention from both physical and computer science communities. Besides the
underlying structure and dynamics of tagging systems, many efforts have been
addressed to unify tagging information to reveal user behaviors and
preferences, extract the latent semantic relations among items, make
recommendations, and so on. Specifically, this article summarizes recent
progress about tag-aware recommender systems, emphasizing on the contributions
from three mainstream perspectives and approaches: network-based methods,
tensor-based methods, and the topic-based methods. Finally, we outline some
other tag-related works and future challenges of tag-aware recommendation
algorithms.Comment: 19 pages, 3 figure
Recommending Items in Social Tagging Systems Using Tag and Time Information
In this work we present a novel item recommendation approach that aims at
improving Collaborative Filtering (CF) in social tagging systems using the
information about tags and time. Our algorithm follows a two-step approach,
where in the first step a potentially interesting candidate item-set is found
using user-based CF and in the second step this candidate item-set is ranked
using item-based CF. Within this ranking step we integrate the information of
tag usage and time using the Base-Level Learning (BLL) equation coming from
human memory theory that is used to determine the reuse-probability of words
and tags using a power-law forgetting function.
As the results of our extensive evaluation conducted on data-sets gathered
from three social tagging systems (BibSonomy, CiteULike and MovieLens) show,
the usage of tag-based and time information via the BLL equation also helps to
improve the ranking and recommendation process of items and thus, can be used
to realize an effective item recommender that outperforms two alternative
algorithms which also exploit time and tag-based information.Comment: 6 pages, 2 tables, 9 figure
Lightweight Tag-Aware Personalized Recommendation on the Social Web Using Ontological Similarity
With the rapid growth of social tagging systems, many research efforts are being put intopersonalized search and recommendation using social tags (i.e., folksonomies). As users can freely choosetheir own vocabulary, social tags can be very ambiguous (for instance, due to the use of homonymsor synonyms). Machine learning techniques (such as clustering and deep neural networks) are usuallyapplied to overcome this tag ambiguity problem. However, the machine-learning-based solutions alwaysneed very powerful computing facilities to train recommendation models from a large amount of data,so they are inappropriate to be used in lightweight recommender systems. In this work, we propose anontological similarity to tackle the tag ambiguity problem without the need of model training by usingcontextual information. The novelty of this ontological similarity is that it first leverages external domainontologies to disambiguate tag information, and then semantically quantifies the relevance between userand item profiles according to the semantic similarity of the matching concepts of tags in the respectiveprofiles. Our experiments show that the proposed ontological similarity is semantically more accurate thanthe state-of-the-art similarity metrics, and can thus be applied to improve the performance of content-based tag-aware personalized recommendation on the Social Web. Consequently, as a model-training-freesolution, ontological similarity is a good disambiguation choice for lightweight recommender systems anda complement to machine-learning-based recommendation solutions.Fil: Xu, Zhenghua. University of Oxford; Reino UnidoFil: Tifrea-Marciuska, Oana. Bloomberg; Reino UnidoFil: Lukasiewicz, Thomas. University of Oxford; Reino UnidoFil: Martinez, Maria Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Simari, Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Ciencias e Ingeniería de la Computación. Universidad Nacional del Sur. Departamento de Ciencias e Ingeniería de la Computación. Instituto de Ciencias e Ingeniería de la Computación; ArgentinaFil: Chen, Cheng. China Academy of Electronics and Information Technology; Chin
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Reusing Ontologies to Enrich Semantically User Content in Web2.0: A Case Study on Folksonomies
Semantic Web and Web2.0 emerged during the past decade promising to achieve new frontiers for the Web. On the one hand, the Semantic Web is an interlinked web of data, supported by ontological semantics and allowing for intelligent applications such as semantic search and integration of heterogeneous content across systems and applications. On the other hand, Web2.0 represents the new technologies and paradigms that revolutionised the user engagement in content creation and introduced novel means towards social interaction. Bridging the gap between Web2.0 and the Semantic Web has been proposed as a means to better manage and interact with the large amounts of user contributed content, which is a new challenge for Web2.0. This thesis focuses on a popular paradigm of Web2.0, folksonomies. In particular, we investigate the semantic enrichment of folksonomy tagspaces by reusing ontologies available in the Semantic Web. We identify the need for methods that automatically apply semantic descriptions to user generated content without requiring user intervention or alteration of the current tagging paradigm. We use an iterative approach in order to identify the characteristics of folksonomies and the attributes of knowledge sources that influence the semantic enrichment of tagspaces. We build on the results of our experimental studies to implement a folksonomy enrichment algorithm, that given an input tagspace, automatically creates a semantic structure that describes the meaning and relations of tags. We introduce measures for the evaluation of enriched tagspaces and finally, we propose a search algorithm that exploits the semantic structures to improve folksonomy search
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