Exploiting the Wisdom of the Crowds for Characterizing and Connecting Heterogeneous Resources

Ricardo Kawase presenting @HT2014 in Santiago, Chile (picture taken by Christoph Trattner)

Heterogeneous content is an inherent problem for cross-system search, recommendation and personalization. In this paper we investigate differences in topic coverage and the impact of topics in different kinds of Web services. We use entity extraction and categorization to create fingerprints that allow for meaningful comparison. As a basis taxonomy, we use the 23 main categories of Wikipedia Category Graph, which has been assembled over the years by the wisdom of the crowds. Following a proof of concept of our approach, we analyze differences in topic coverage and topic impact. The results show many differences between Web services like Twitter, Flickr and Delicious, which reflect users’ behavior and the usage of each system. The paper concludes with a user study that demonstrates the benefits of fingerprints over traditional textual methods for recommendations of heterogeneous resources.

Authors: Ricardo Kawase, Patrick Siehndel, Bernardo Pereira Nunes, Eelco Herder and Wolfgang Nejdl

PDF: kawase-ht2014

Online Prototype: http://twikime.l3s.uni-hannover.de

 

TwikiMe! User profiles that make sense.

The use of social media has been rapidly increasing in the last years. Social media, such as Twitter, has become an important source of information for a variety of people. The public availability of data describing some of these social networks has led to a great deal of research in this area. Link prediction, user classification and community detection are some of the main research areas related to social networks. In this paper, we present a user modeling framework that uses Wikipedia to model user interests inside a social network. Our model of user interests reflects the areas a user is interested in, as well as the level of expertise a user has in a certain field.

Authors: Patrick Siehndel, Ricardo Kawase

PDF: siehndel-iswc2012

Online Prototype: http://twikime.l3s.uni-hannover.de/twikime.php

Exploiting Twitter as a Social Channel for Human Computation

To fully leverage the innate problem solving capabilities of humans necessitates paradigm shifts towards decentralization of human computation
systems, making the existence of central authorities super uous and even impossible. In this position paper, we propose a novel decentralized
architecture that exploits the Twitter social network as a communication channel for harnessing human computation. Our framework provides
individuals and organizations the necessary infrastructure for human computation, facilitating human task submission, assignment and
aggregation. We presented a proof of concept and explore the feasibility of our approach in the light of several use cases.

Venue: WWW2012, CrowdSearch2012

Authors: Ernesto Diaz-Aviles, Ricardo Kawase, Wolfgang Nejdl

PDF: diaz-www12-CrowdSearch12