given name for their child. This choice is influenced by different factors,
such as the social context, language, cultural background and especially
personal taste. Although this task is omnipresent, little research has been
conducted on the analysis and application of interrelations among given names
from a data mining perspective.
The present work tackles the problem of recommending given names, by firstly
mining for inter-name relatedness in data from the Social Web. Based on these
results, the name search engine "Nameling" was built, which attracted more than
35,000 users within less than six months, underpinning the relevance of the
underlying recommendation task. The accruing usage data is then used for
evaluating different state-of-the-art recommendation systems, as well our new
NR algorithm which we adopted from our previous work on folksonomies and which
yields the best results, considering the trade-off between prediction accuracy
and runtime performance as well as its ability to generate personalized
recommendations. We also show, how the gathered inter-name relationships can be
used for meaningful result diversification of PageRank-based recommendation
systems.
As all of the considered usage data is made publicly available, the present
work establishes baseline results, encouraging other researchers to implement
advanced recommendation systems for given names. ER - TY - CHAP AU - Jäschke, Robert AU - Hotho, Andreas AU - Mitzlaff, Folke AU - Stumme, Gerd A2 - Kacprzyk, Janusz A2 - Jain, Lakhmi C. T1 - Challenges in Tag Recommendations for Collaborative Tagging Systems T2 - Recommender Systems for the Social Web PB - Springer CY - Berlin/Heidelberg PY - 2012/ VL - 32 IS - SP - 65 EP - 87 UR - http://dx.doi.org/10.1007/978-3-642-25694-3_3 M3 - 10.1007/978-3-642-25694-3_3 KW - challenge KW - collaborative KW - recommendation KW - system KW - tagging L1 - SN - 978-3-642-25694-3 N1 - N1 - AB - Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags . Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area. ER - TY - JOUR AU - Noy, N F AU - Chugh, A AU - Alani, H T1 - The CKC Challenge: Exploring Tools for Collaborative Knowledge Construction JO - IEEE Intell Syst PY - 2008/1 VL - 23 IS - 1 SP - 64 EP - 68 UR - http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3966208/ M3 - 10.1109/MIS.2008.14 KW - aboutBibSonomy KW - bibsonomy KW - challenge KW - ckc KW - expectation KW - tagging KW - tools KW - user L1 - SN - N1 - The CKC Challenge: Exploring Tools for Collaborative Knowledge Construction N1 - AB - The great success of Web 2.0 is mainly fuelled by an infrastructure that allows web users to create, share, tag, and connect content and knowledge easily. The tools for developing structured knowledge in this manner have started to appear as well. However, there are few, if any, user studies that are aimed at understanding what users expect from such tools, what works and what doesn't. We organized the Collaborative Knowledge Construction (CKC) Challenge to assess the state of the art for the tools that support collaborative processes for creation of various forms of structured knowledge. The goal of the Challenge was to get users to try out different tools and to learn what users expect from such tools-features that users need, features that they like or dislike. The Challenge task was to construct structured knowledge for a portal that would provide information about research. The Challenge design contained several incentives for users to participate. Forty-nine users registered for the Challenge; thirty-three of them participated actively by using the tools. We collected extensive feedback from the users where they discussed their thoughts on all the tools that they tried. In this paper, we present the results of the Challenge, discuss the features that users expect from tools for collaborative knowledge constructions, the features on which Challenge participants disagreed, and the lessons that we learned. ER -