356 research outputs found
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People-Powered Music: Using User-Generated Tags and Structure in Recommendations
Music recommenders often rely on experts to classify song facets like genre and mood, but user-generated folksonomies hold some advantages over expert classifications—folksonomies can reflect the same real-world vocabularies and categorizations that end users employ. We present an approach for using crowd-sourced common sense knowledge to structure user-generated music tags into a folksonomy, and describe how to use this approach to make music recommendations. We then empirically evaluate our “people-powered” structured content recommender against a more traditional recommender. Our results show that participants slightly preferred the unstructured recommender, rating more of its recommendations as “perfect” than they did for our approach. An exploration of the reasons behind participants’ ratings revealed that users behaved differently when tagging songs than when evaluating recommendations, and we discuss the implications of our results for future tagging and recommendation approaches
A framework for dataset benchmarking and its application to a new movie rating dataset
This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in ACM Transactions on Intelligent Systems and Technology, http://dx.doi.org/10.1145/2751565Rating datasets are of paramount importance in recommender systems research. They serve as input for recommendation algorithms, as simulation data, or for evaluation purposes. In the past, public accessible rating datasets were not abundantly available, leaving researchers no choice but to work with old and static datasets like MovieLens and Netflix. More recently, however, emerging trends as social media and smart-phones are found to provide rich data sources which can be turned into valuable research datasets. While dataset availability is growing, a structured way for introducing and comparing new datasets is currently still lacking. In this work, we propose a five-step framework to introduce and benchmark new datasets in the recommender systems domain. We illustrate our framework on a new movie rating dataset-called Movie Tweetings-collected from Twitter. Following our framework, we detail the origin of the dataset, provide basic descriptive statistics, investigate external validity, report the results of a number of reproducible benchmarks, and conclude by discussing some interesting advantages and appropriate research use cases.This work is funded by a PhD grant to Simon Dooms of the Agency for Innovation by Science and Technology (IWT Vlaanderen) and the Spanish Ministry of Science and Innovation (TIN2013-47090-C3-2). Part of this work was carried out during the tenure of an ERCIM "Alain Bensoussan" Fellowship Programme, funded by European Comission FP7 grant agreement no. 246016. The experiments in this work were carried out using the Stevin Supercomputer Infrastructure at Ghent University, funded by Ghent University, the Hercules Foundation, and the Flemish Government - department EWI
Content-awareness and graph-based ranking for tag recommendation in folksonomies
Tag recommendation algorithms aid the social tagging process in many userdriven
document indexing applications, such as social bookmarking and publication
sharing websites. This thesis gives an overview of existing tag recommendation
methods and proposes novel approaches that address the new document problem
and the task of ranking tags. The focus is on graph-based methods such as Folk-
Rank that apply weight spreading algorithms to a graph representation of the folksonomy.
In order to suggest tags for previously untagged documents, extensions are
presented that introduce content into the recommendation process as an additional
information source. To address the problem of ranking tags, an in-depth analysis
of graph models as well as ranking algorithms is conducted. Implicit assumptions
made by the widely-used graph model of the folksonomy are highlighted and an
improved model is proposed that captures the characteristics of the social tagging
data more accurately. Additionally, issues in the tag rank computation of FolkRank
are analysed and an adapted weight spreading approach for social tagging data is
presented. Moreover, the applicability of conventional weight spreading methods to
data from the social tagging domain is examined in detail. Finally, indications of
implicit negative feedback in the data structure of folksonomies are analysed and
novel approaches of identifying negative relationships are presented. By exploiting
the three-dimensional characteristics of social tagging data the proposed metrics are
based on stronger evidence and provide reliable measures of negative feedback.
Including content into the tag recommendation process leads to a significant
increase in recommendation accuracy on real-world datasets. The proposed adaptations
to graph models and ranking algorithms result in more accurate and computationally
less expensive recommenders. Moreover, new insights into the fundamental
characteristics of social tagging data are revealed and a novel data interpretation
that takes negative feedback into account is proposed
Deliverable D.8.4. Social Data Visualization and Navigation Services:3rd Year Update
Within the Open Discovery Space our study (T.8.4) focused on ”Enhanced Social Data Visualization & Navigation Services. This deliverable provides the prototype report regarding the deployment of adapted visualization and navigation services to be integrated in the ODS Social Data Management Layer.Project co-funded by the European Commission within the ICT Policy Support Programme, CIP Competitiveness and innovation framework programme 2007 - 2013. Grant agreement no: 29722
Evaluating Feature-Specific Similarity Metrics using Human Judgments for Norwegian News
Masteroppgave i informasjonsvitenskapINFO390MASV-INF
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