PUMA publications for /tag/collaborativehttps://puma.uni-kassel.de/tag/collaborativePUMA RSS feed for /tag/collaborative2024-03-28T09:03:00+01:00- Searching social networkshttps://puma.uni-kassel.de/bibtex/2c6b422948459e04a86e766055608e55e/jaeschkejaeschke2012-10-11T17:44:39+02:00agent collaborative network search social web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bin Yu" itemprop="url" href="/author/Bin%20Yu"><span itemprop="name">B. Yu</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Munindar P. Singh" itemprop="url" href="/author/Munindar%20P.%20Singh"><span itemprop="name">M. Singh</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the second international joint conference on Autonomous agents and multiagent systems</span>, </em></span><em>Seite <span itemprop="pagination">65--72</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Thu Oct 11 17:44:39 CEST 2012New York, NY, USAProceedings of the second international joint conference on Autonomous agents and multiagent systems65--72Searching social networks2003agent collaborative network search social web A referral system is a multiagent system whose member agents are capable of giving and following referrals. The specific cases of interest arise where each agent has a user. The agents cooperate by giving and taking referrals so each can better help its user locate relevant information. This use of referrals mimics human interactions and can potentially lead to greater effectiveness and efficiency than in single-agent systems.Existing approaches consider what referrals may be given and treat the referring process simply as path search in a static graph. By contrast, the present approach understands referrals as arising in and influencing dynamic social networks, where the agents act autonomously based on local knowledge. This paper studies strategies using which agents may search dynamic social networks. It evaluates the proposed approach empirically for a community of AI scientists (partially derived from bibliographic data). Further, it presents a prototype system that assists users in finding other users in practical social networks.
- Referral Web: combining social networks and collaborative filteringhttps://puma.uni-kassel.de/bibtex/2832d16a8c86e769c7ac9ace5381f757e/jaeschkejaeschke2012-12-05T16:50:59+01:00collaborative filtering hybrid network recommender social stair <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Henry Kautz" itemprop="url" href="/author/Henry%20Kautz"><span itemprop="name">H. Kautz</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bart Selman" itemprop="url" href="/author/Bart%20Selman"><span itemprop="name">B. Selman</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Mehul Shah" itemprop="url" href="/author/Mehul%20Shah"><span itemprop="name">M. Shah</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Communications of the ACM</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">40 </span></span>(<span itemprop="issueNumber">3</span>):
<span itemprop="pagination">63--65</span></em> </span>(<em><span>März 1997<meta content="März 1997" itemprop="datePublished"/></span></em>)Wed Dec 05 16:50:59 CET 2012New York, NY, USACommunications of the ACMmar363--65Referral Web: combining social networks and collaborative filtering401997collaborative filtering hybrid network recommender social stair
- Recommender Systems for Social Tagging Systemshttps://puma.uni-kassel.de/bibtex/287d6883ebd98e8810be45d7e7e4ade96/stummestumme2013-03-18T14:06:44+01:002012 bookmarking collaborative folksonomy info20 itegpub l3s myown recommender social tagging tagging,2012 <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="L. Balby Marinho" itemprop="url" href="/author/L.%20Balby%20Marinho"><span itemprop="name">L. Balby Marinho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Hotho" itemprop="url" href="/author/A.%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="R. Jäschke" itemprop="url" href="/author/R.%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="A. Nanopoulos" itemprop="url" href="/author/A.%20Nanopoulos"><span itemprop="name">A. Nanopoulos</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="S. Rendle" itemprop="url" href="/author/S.%20Rendle"><span itemprop="name">S. Rendle</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="L. Schmidt-Thieme" itemprop="url" href="/author/L.%20Schmidt-Thieme"><span itemprop="name">L. Schmidt-Thieme</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="G. Stumme" itemprop="url" href="/author/G.%20Stumme"><span itemprop="name">G. Stumme</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="P. Symeonidis" itemprop="url" href="/author/P.%20Symeonidis"><span itemprop="name">P. Symeonidis</span></a></span>. </span><em>SpringerBriefs in Electrical and Computer Engineering </em><em><span itemprop="publisher">Springer</span>, </em>(<em><span>Februar 2012<meta content="Februar 2012" itemprop="datePublished"/></span></em>)Mon Mar 18 14:06:44 CET 2013febSpringerBriefs in Electrical and Computer EngineeringRecommender Systems for Social Tagging Systems20122012 bookmarking collaborative folksonomy info20 itegpub l3s myown recommender social tagging tagging,2012 Social Tagging Systems are web applications in which users upload resources (e.g., bookmarks, videos, photos, etc.) and annotate it with a list of freely chosen keywords called tags. This is a grassroots approach to organize a site and help users to find the resources they are interested in. Social tagging systems are open and inherently social; features that have been proven to encourage participation. However, with the large popularity of these systems and the increasing amount of user-contributed content, information overload rapidly becomes an issue. Recommender Systems are well known applications for increasing the level of relevant content over the “noise” that continuously grows as more and more content becomes available online. In social tagging systems, however, we face new challenges. While in classic recommender systems the mode of recommendation is basically the resource, in social tagging systems there are three possible modes of recommendation: users, resources, or tags. Therefore suitable methods that properly exploit the different dimensions of social tagging systems data are needed. In this book, we survey the most recent and state-of-the-art work about a whole new generation of recommender systems built to serve social tagging systems. The book is divided into self-contained chapters covering the background material on social tagging systems and recommender systems to the more advanced techniques like the ones based on tensor factorization and graph-based models.
- Probabilistic models for unified collaborative and content-based recommendation in sparse-data environmentshttps://puma.uni-kassel.de/bibtex/2407a9c070710c3c3b7fe307d384aed37/jaeschkejaeschke2012-12-06T17:23:37+01:00collaborative content probabilistic recommender sparse <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Alexandrin Popescul" itemprop="url" href="/author/Alexandrin%20Popescul"><span itemprop="name">A. Popescul</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="David M. Pennock" itemprop="url" href="/author/David%20M.%20Pennock"><span itemprop="name">D. Pennock</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Steve Lawrence" itemprop="url" href="/author/Steve%20Lawrence"><span itemprop="name">S. Lawrence</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence</span>, </em></span><em>Seite <span itemprop="pagination">437--444</span>. </em><em>San Francisco, CA, USA, </em><em><span itemprop="publisher">Morgan Kaufmann Publishers Inc.</span>, </em>(<em><span>2001<meta content="2001" itemprop="datePublished"/></span></em>)Thu Dec 06 17:23:37 CET 2012San Francisco, CA, USAProceedings of the Seventeenth conference on Uncertainty in artificial intelligence437--444Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments2001collaborative content probabilistic recommender sparse Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmarm's (1999) aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not imposed as an exogenous parameter, but rather emerges naturally from the given data sources. However, global probabilistic models coupled with standard EM learning algorithms tend to drastically overfit in the sparsedata situations typical of recommendation applications. We show that secondary content information can often be used to overcome sparsity. Experiments on data from the Researchlndex library of Computer Science publications show that appropriate mixture models incorporating secondary data produce significantly better quality recommenders than <i>k</i>-nearest neighbors (<i>k</i>-NN). Global probabilistic models also allow more general inferences than local methods like <i>k</i>-NN.
- Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendationhttps://puma.uni-kassel.de/bibtex/264bf590675a833770b7d284871435a8d/stummestumme2013-03-18T14:06:44+01:002012 bookmarking collaborative folkrank itegpub l3s myown recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gerd Stumme" itemprop="url" href="/author/Gerd%20Stumme"><span itemprop="name">G. Stumme</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web</span>, </em></span><em>Seite <span itemprop="pagination">9--16</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>September 2012<meta content="September 2012" itemprop="datePublished"/></span></em>)Mon Mar 18 14:06:44 CET 2013New York, NY, USAProceedings of the 4th ACM RecSys workshop on Recommender systems and the social websep9--16Leveraging Publication Metadata and Social Data into FolkRank for Scientific Publication Recommendation 20122012 bookmarking collaborative folkrank itegpub l3s myown recommender social tagging The ever-growing flood of new scientific articles requires novel retrieval mechanisms. One means for mitigating this instance of the information overload phenomenon are collaborative tagging systems, that allow users to select, share and annotate references to publications. These systems employ recommendation algorithms to present to their users personalized lists of interesting and relevant publications. In this paper we analyze different ways to incorporate social data and metadata from collaborative tagging systems into the graph-based ranking algorithm FolkRank to utilize it for recommending scientific articles to users of the social bookmarking system BibSonomy. We compare the results to those of Collaborative Filtering, which has previously been applied for resource recommendation.
- Item-based collaborative filtering recommendation algorithmshttps://puma.uni-kassel.de/bibtex/216f38785d7829500ed41c610a5eff9a2/jaeschkejaeschke2013-04-16T07:46:25+02:00cf collaborative filtering item recommender <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Badrul Sarwar" itemprop="url" href="/author/Badrul%20Sarwar"><span itemprop="name">B. Sarwar</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="George Karypis" itemprop="url" href="/author/George%20Karypis"><span itemprop="name">G. Karypis</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Joseph Konstan" itemprop="url" href="/author/Joseph%20Konstan"><span itemprop="name">J. Konstan</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="John Riedl" itemprop="url" href="/author/John%20Riedl"><span itemprop="name">J. Riedl</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 10th international conference on World Wide Web</span>, </em></span><em>Seite <span itemprop="pagination">285--295</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2001<meta content="2001" itemprop="datePublished"/></span></em>)Tue Apr 16 07:46:25 CEST 2013New York, NY, USAProceedings of the 10th international conference on World Wide Web285--295Item-based collaborative filtering recommendation algorithms2001cf collaborative filtering item recommender
- Finding high-quality content in social mediahttps://puma.uni-kassel.de/bibtex/229c5c74d95dce215a9692b94fc619839/jaeschkejaeschke2012-10-11T17:41:54+02:00answering collaborative media quality question reputation search social web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Eugene Agichtein" itemprop="url" href="/author/Eugene%20Agichtein"><span itemprop="name">E. Agichtein</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Carlos Castillo" itemprop="url" href="/author/Carlos%20Castillo"><span itemprop="name">C. Castillo</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Debora Donato" itemprop="url" href="/author/Debora%20Donato"><span itemprop="name">D. Donato</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Aristides Gionis" itemprop="url" href="/author/Aristides%20Gionis"><span itemprop="name">A. Gionis</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Gilad Mishne" itemprop="url" href="/author/Gilad%20Mishne"><span itemprop="name">G. Mishne</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the international conference on Web search and web data mining</span>, </em></span><em>Seite <span itemprop="pagination">183--194</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2008<meta content="2008" itemprop="datePublished"/></span></em>)Thu Oct 11 17:41:54 CEST 2012New York, NY, USAProceedings of the international conference on Web search and web data mining183--194Finding high-quality content in social media2008answering collaborative media quality question reputation search social web The quality of user-generated content varies drastically from excellent to abuse and spam. As the availability of such content increases, the task of identifying high-quality content sites based on user contributions --social media sites -- becomes increasingly important. Social media in general exhibit a rich variety of information sources: in addition to the content itself, there is a wide array of non-content information available, such as links between items and explicit quality ratings from members of the community. In this paper we investigate methods for exploiting such community feedback to automatically identify high quality content. As a test case, we focus on Yahoo! Answers, a large community question/answering portal that is particularly rich in the amount and types of content and social interactions available in it. We introduce a general classification framework for combining the evidence from different sources of information, that can be tuned automatically for a given social media type and quality definition. In particular, for the community question/answering domain, we show that our system is able to separate high-quality items from the rest with an accuracy close to that of humansFinding high-quality content in social media
- Evaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULikehttps://puma.uni-kassel.de/bibtex/242773258c36ccf2f59749991518d1784/stephandoerfelstephandoerfel2013-01-18T11:32:21+01:00algorithms citedBy:doerfel2012leveraging collaborative evaluation filtering <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Denis Parra" itemprop="url" href="/author/Denis%20Parra"><span itemprop="name">D. Parra</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Peter Brusilovsky" itemprop="url" href="/author/Peter%20Brusilovsky"><span itemprop="name">P. Brusilovsky</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social Web</span>, </em></span><em>Volume 467 von CEUR Workshop Proceedings, </em>(<em><span>Juni 2009<meta content="Juni 2009" itemprop="datePublished"/></span></em>)Fri Jan 18 11:32:21 CET 2013Proceedings of the Workshop on Web 3.0: Merging Semantic Web and Social WebjunCEUR Workshop ProceedingsEvaluation of Collaborative Filtering Algorithms for Recommending Articles on CiteULike4672009algorithms citedBy:doerfel2012leveraging collaborative evaluation filtering Motivated by the potential use of collaborative tagging systems to develop new recommender systems, we have implemented and compared three variants of user-based collaborative filtering algorithms to provide recommendations of articles on CiteULike. On our first approach, Classic Collaborative filtering (CCF), we use Pearson correlation to calculate similarity between users and a classic adjusted ratings formula to rank the recommendations. Our second approach, Neighbor-weighted Collaborative Filtering (NwCF), incorporates the amount of raters in the ranking formula of the recommendations. A modified version of the Okapi BM25 IR model over users ’ tags is implemented on our third approach to form the user neighborhood. Our results suggest that incorporating the number of raters into the algorithms leads to an improvement of precision, and they also support that tags can be considered as an alternative to Pearson correlation to calculate the similarity between users and their neighbors in a collaborative tagging system.
- Do you want to take notes?: identifying research missions in Yahoo! search padhttps://puma.uni-kassel.de/bibtex/22e2c2c1d1b7fcd30f11cbde5729f554e/jaeschkejaeschke2012-10-12T09:03:13+02:00collaborative pad search social web yahoo <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Debora Donato" itemprop="url" href="/author/Debora%20Donato"><span itemprop="name">D. Donato</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Francesco Bonchi" itemprop="url" href="/author/Francesco%20Bonchi"><span itemprop="name">F. Bonchi</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tom Chi" itemprop="url" href="/author/Tom%20Chi"><span itemprop="name">T. Chi</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yoelle Maarek" itemprop="url" href="/author/Yoelle%20Maarek"><span itemprop="name">Y. Maarek</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 19th international conference on World wide web</span>, </em></span><em>Seite <span itemprop="pagination">321--330</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2010<meta content="2010" itemprop="datePublished"/></span></em>)Fri Oct 12 09:03:13 CEST 2012New York, NY, USAProceedings of the 19th international conference on World wide web321--330Do you want to take notes?: identifying research missions in Yahoo! search pad2010collaborative pad search social web yahoo Addressing user's information needs has been one of the main goals of Web search engines since their early days. In some cases, users cannot see their needs immediately answered by search results, simply because these needs are too complex and involve multiple aspects that are not covered by a single Web or search results page. This typically happens when users investigate a certain topic in domains such as education, travel or health, which often require collecting facts and information from many pages. We refer to this type of activities as "research missions". These research missions account for 10% of users' sessions and more than 25% of all query volume, as verified by a manual analysis that was conducted by Yahoo! editors.</p> <p>We demonstrate in this paper that such missions can be automatically identified on-the-fly, as the user interacts with the search engine, through careful runtime analysis of query flows and query sessions.</p> <p>The on-the-fly automatic identification of research missions has been implemented in Search Pad, a novel Yahoo! application that was launched in 2009, and that we present in this paper. Search Pad helps users keeping trace of results they have consulted. Its novelty however is that unlike previous notes taking products, it is automatically triggered only when the system decides, with a fair level of confidence, that the user is undertaking a research mission and thus is in the right context for gathering notes. Beyond the Search Pad specific application, we believe that changing the level of granularity of query modeling, from an isolated query to a list of queries pertaining to the same research missions, so as to better reflect a certain type of information needs, can be beneficial in a number of other Web search applications. Session-awareness is growing and it is likely to play, in the near future, a fundamental role in many on-line tasks: this paper presents a first step on this path.
- Discovering authorities in question answer communities by using link analysishttps://puma.uni-kassel.de/bibtex/235394620d2654db8543d5da60f6f00dc/jaeschkejaeschke2012-10-11T17:43:00+02:00answering collaborative quality question reputation search social web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Pawel Jurczyk" itemprop="url" href="/author/Pawel%20Jurczyk"><span itemprop="name">P. Jurczyk</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Eugene Agichtein" itemprop="url" href="/author/Eugene%20Agichtein"><span itemprop="name">E. Agichtein</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the sixteenth ACM conference on Conference on information and knowledge management</span>, </em></span><em>Seite <span itemprop="pagination">919--922</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Thu Oct 11 17:43:00 CEST 2012New York, NY, USAProceedings of the sixteenth ACM conference on Conference on information and knowledge management919--922Discovering authorities in question answer communities by using link analysis2007answering collaborative quality question reputation search social web Question-Answer portals such as Naver and Yahoo! Answers are quickly becoming rich sources of knowledge on many topics which are not well served by general web search engines. Unfortunately, the quality of the submitted answers is uneven, ranging from excellent detailed answers to snappy and insulting remarks or even advertisements for commercial content. Furthermore, user feedback for many topics is sparse, and can be insufficient to reliably identify good answers from the bad ones. Hence, estimating the authority of users is a crucial task for this emerging domain, with potential applications to answer ranking, spam detection, and incentive mechanism design. We present an analysis of the link structure of a general-purpose question answering community to discover authoritative users, and promising experimental results over a dataset of more than 3 million answers from a popular community QA site. We also describe structural differences between question topics that correlate with the success of link analysis for authority discovery.
- Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendationshttps://puma.uni-kassel.de/bibtex/2e585a92994be476480545eb62d741642/stummestumme2013-12-16T17:19:49+01:002013 bookmarking collaborative folkrank folksonomy graph iteg itegpub l3s recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nikolas Landia" itemprop="url" href="/author/Nikolas%20Landia"><span itemprop="name">N. Landia</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sarabjot Singh Anand" itemprop="url" href="/author/Sarabjot%20Singh%20Anand"><span itemprop="name">S. Anand</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nathan Griffiths" itemprop="url" href="/author/Nathan%20Griffiths"><span itemprop="name">N. Griffiths</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>cs.IR</em></span></span> </span>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Mon Dec 16 17:19:49 CET 2013cs.IRDeeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations1310.149820132013 bookmarking collaborative folkrank folksonomy graph iteg itegpub l3s recommender social tagging The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.
- Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendationshttps://puma.uni-kassel.de/bibtex/2e585a92994be476480545eb62d741642/stephandoerfelstephandoerfel2013-10-13T05:19:47+02:002013 bookmarking collaborative folkrank folksonomy graph myown <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nikolas Landia" itemprop="url" href="/author/Nikolas%20Landia"><span itemprop="name">N. Landia</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sarabjot Singh Anand" itemprop="url" href="/author/Sarabjot%20Singh%20Anand"><span itemprop="name">S. Anand</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nathan Griffiths" itemprop="url" href="/author/Nathan%20Griffiths"><span itemprop="name">N. Griffiths</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>cs.IR</em></span></span> </span>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Sun Oct 13 05:19:47 CEST 2013cs.IRDeeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations1310.149820132013 bookmarking collaborative folkrank folksonomy graph myown The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.
- Deeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendationshttps://puma.uni-kassel.de/bibtex/2e585a92994be476480545eb62d741642/hothohotho2013-10-10T12:27:01+02:002013 bookmarking collaborative folkrank folksonomy graph myown recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nikolas Landia" itemprop="url" href="/author/Nikolas%20Landia"><span itemprop="name">N. Landia</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Sarabjot Singh Anand" itemprop="url" href="/author/Sarabjot%20Singh%20Anand"><span itemprop="name">S. Anand</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Andreas Hotho" itemprop="url" href="/author/Andreas%20Hotho"><span itemprop="name">A. Hotho</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Nathan Griffiths" itemprop="url" href="/author/Nathan%20Griffiths"><span itemprop="name">N. Griffiths</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>cs.IR</em></span></span> </span>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Thu Oct 10 12:27:01 CEST 2013cs.IRDeeper Into the Folksonomy Graph: FolkRank Adaptations and Extensions for Improved Tag Recommendations1310.149820132013 bookmarking collaborative folkrank folksonomy graph myown recommender social tagging The information contained in social tagging systems is often modelled as a graph of connections between users, items and tags. Recommendation algorithms such as FolkRank, have the potential to leverage complex relationships in the data, corresponding to multiple hops in the graph. We present an in-depth analysis and evaluation of graph models for social tagging data and propose novel adaptations and extensions of FolkRank to improve tag recommendations. We highlight implicit assumptions made by the widely used folksonomy model, and propose an alternative and more accurate graph-representation of the data. Our extensions of FolkRank address the new item problem by incorporating content data into the algorithm, and significantly improve prediction results on unpruned datasets. Our adaptations address issues in the iterative weight spreading calculation that potentially hinder FolkRank's ability to leverage the deep graph as an information source. Moreover, we evaluate the benefit of considering each deeper level of the graph, and present important insights regarding the characteristics of social tagging data in general. Our results suggest that the base assumption made by conventional weight propagation methods, that closeness in the graph always implies a positive relationship, does not hold for the social tagging domain.
- Content-boosted Collaborative Filtering for Improved Recommendationshttps://puma.uni-kassel.de/bibtex/2a4917f0299f48e403966a8003ebd50be/hothohotho2015-02-16T17:25:55+01:00collaborative content filtering hybrid recommender <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Prem Melville" itemprop="url" href="/author/Prem%20Melville"><span itemprop="name">P. Melville</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Raymod J. Mooney" itemprop="url" href="/author/Raymod%20J.%20Mooney"><span itemprop="name">R. Mooney</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ramadass Nagarajan" itemprop="url" href="/author/Ramadass%20Nagarajan"><span itemprop="name">R. Nagarajan</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Eighteenth National Conference on Artificial Intelligence</span>, </em></span><em>Seite <span itemprop="pagination">187--192</span>. </em><em>Menlo Park, CA, USA, </em><em><span itemprop="publisher">American Association for Artificial Intelligence</span>, </em>(<em><span>2002<meta content="2002" itemprop="datePublished"/></span></em>)Mon Feb 16 17:25:55 CET 2015Menlo Park, CA, USAEighteenth National Conference on Artificial Intelligence187--192Content-boosted Collaborative Filtering for Improved Recommendations2002collaborative content filtering hybrid recommender Most recommender systems use Collaborative Filtering or Content-based methods to predict new items of interest for a user. While both methods have their own advantages, individually they fail to provide good recommendations in many situations. Incorporating components from both methods, a hybrid recommender system can overcome these shortcomings. In this paper, we present an elegant and effective framework for combining content and collaboration. Our approach uses a content-based predictor tc enhance existing user data, and then provides personalized suggestions through collaborative filtering. We present experimental results that show how this approach, <i>Content-Boosted Collaborative Filtering</i>, performs better than a pure content-based predictor, pure collaborative filter, and a naive hybrid approach.
- Collaborative filtering with temporal dynamicshttps://puma.uni-kassel.de/bibtex/2dad3f9050f58acf0551924e537e84e45/jaeschkejaeschke2012-12-10T09:23:31+01:00cf collaborative dynamics filtering netflix stair temporal <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Yehuda Koren" itemprop="url" href="/author/Yehuda%20Koren"><span itemprop="name">Y. Koren</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining</span>, </em></span><em>Seite <span itemprop="pagination">447--456</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Mon Dec 10 09:23:31 CET 2012New York, NY, USAProceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining447--456Collaborative filtering with temporal dynamics2009cf collaborative dynamics filtering netflix stair temporal Customer preferences for products are drifting over time. Product perception and popularity are constantly changing as new selection emerges. Similarly, customer inclinations are evolving, leading them to ever redefine their taste. Thus, modeling temporal dynamics should be a key when designing recommender systems or general customer preference models. However, this raises unique challenges. Within the eco-system intersecting multiple products and customers, many different characteristics are shifting simultaneously, while many of them influence each other and often those shifts are delicate and associated with a few data instances. This distinguishes the problem from concept drift explorations, where mostly a single concept is tracked. Classical time-window or instance-decay approaches cannot work, as they lose too much signal when discarding data instances. A more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. The paradigm we offer is creating a model tracking the time changing behavior throughout the life span of the data. This allows us to exploit the relevant components of all data instances, while discarding only what is modeled as being irrelevant. Accordingly, we revamp two leading collaborative filtering recommendation approaches. Evaluation is made on a large movie rating dataset by Netflix. Results are encouraging and better than those previously reported on this dataset.
- Characterizing a social bookmarking and tagging networkhttps://puma.uni-kassel.de/bibtex/202d6739886a13180dd92fbb7243ab58b/jaeschkejaeschke2013-05-09T10:47:35+02:00analysis bookmarking collaborative folksonomy network tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ralitsa Angelova" itemprop="url" href="/author/Ralitsa%20Angelova"><span itemprop="name">R. Angelova</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Marek Lipczak" itemprop="url" href="/author/Marek%20Lipczak"><span itemprop="name">M. Lipczak</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Evangelos Milios" itemprop="url" href="/author/Evangelos%20Milios"><span itemprop="name">E. Milios</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Paweł Prałat" itemprop="url" href="/author/Pawe%c5%82%20Pra%c5%82at"><span itemprop="name">P. Prałat</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the Mining Social Data Workshop (MSoDa)</span>, </em></span><em>Seite <span itemprop="pagination">21--25</span>. </em><em>ECAI 2008, </em>(<em><span>Juli 2008<meta content="Juli 2008" itemprop="datePublished"/></span></em>)Thu May 09 10:47:35 CEST 2013Proceedings of the Mining Social Data Workshop (MSoDa)jul21--25Characterizing a social bookmarking and tagging network2008analysis bookmarking collaborative folksonomy network tagging Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests. del.icio.us is a social network where people store and share their personal bookmarks. Most importantly, users tag their bookmarks for ease of information dissemination and later look up. However, it is the friendship links, that make delicious a social network. They exist independently of the set of bookmarks that belong to the users and have no relation to the tags typically assigned to the bookmarks. To study the interaction among users, the strength of the existing links and their hidden meaning, we introduce implicit links in the network. These links connect only highly "similar" users. Here, similarity can reflect different aspects of the user’s profile that makes her similar to any other user, such as number of shared bookmarks, or similarity of their tags clouds. We investigate the question whether friends have common interests, we gain additional insights on the strategies that users use to assign tags to their bookmarks, and we demonstrate that the graphs formed by implicit links have unique properties differing from binomial random graphs or random graphs with an expected power-law degree distribution.
- Augmented social cognition: using social web technology to enhance the ability of groups to remember, think, and reasonhttps://puma.uni-kassel.de/bibtex/2d09b484b1036ca8273743cac1992dd7f/jaeschkejaeschke2012-10-12T09:08:53+02:00bookmarking cognition collaborative collective intelligence search social tagging web wiki <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ed H. Chi" itemprop="url" href="/author/Ed%20H.%20Chi"><span itemprop="name">E. Chi</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 2009 ACM SIGMOD International Conference on Management of data</span>, </em></span><em>Seite <span itemprop="pagination">973--984</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2009<meta content="2009" itemprop="datePublished"/></span></em>)Fri Oct 12 09:08:53 CEST 2012New York, NY, USAProceedings of the 2009 ACM SIGMOD International Conference on Management of data973--984Augmented social cognition: using social web technology to enhance the ability of groups to remember, think, and reason2009bookmarking cognition collaborative collective intelligence search social tagging web wiki We are experiencing a new Social Web, where people share, communicate, commiserate, and conflict with each other. As evidenced by systems like Wikipedia, twitter, and delicious.com, these environments are turning people into social information foragers and sharers. Groups interact to resolve conflicts and jointly make sense of topic areas from "Obama vs. Clinton" to "Islam."</p> <p>PARC's Augmented Social Cognition researchers -- who come from cognitive psychology, computer science, HCI, CSCW, and other disciplines -- focus on understanding how to "enhance a group of people's ability to remember, think, and reason". Through Social Web systems like social bookmarking sites, blogs, Wikis, and more, we can finally study, in detail, these types of enhancements on a very large scale.</p> <p>Here we summarize recent work and early findings such as: (1) how conflict and coordination have played out in Wikipedia, and how social transparency might affect reader trust; (2) how decreasing interaction costs might change participation in social tagging systems; and (3) how computation can help organize user-generated content and metadata.
- An analysis of tag-recommender evaluation procedureshttps://puma.uni-kassel.de/bibtex/2aa4b3d79a362d7415aaa77625b590dfa/stummestumme2013-12-16T17:19:49+01:002013 bibsonomy bookmarking collaborative core evaluation folkrank folksonomy graph iteg itegpub l3s recommender social tagging <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Stephan Doerfel" itemprop="url" href="/author/Stephan%20Doerfel"><span itemprop="name">S. Doerfel</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Robert Jäschke" itemprop="url" href="/author/Robert%20J%c3%a4schke"><span itemprop="name">R. Jäschke</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 7th ACM conference on Recommender systems</span>, </em></span><em>Seite <span itemprop="pagination">343--346</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2013<meta content="2013" itemprop="datePublished"/></span></em>)Mon Dec 16 17:19:49 CET 2013New York, NY, USAProceedings of the 7th ACM conference on Recommender systems343--346RecSys '13An analysis of tag-recommender evaluation procedures20132013 bibsonomy bookmarking collaborative core evaluation folkrank folksonomy graph iteg itegpub l3s recommender social tagging Since the rise of collaborative tagging systems on the web, the tag recommendation task -- suggesting suitable tags to users of such systems while they add resources to their collection -- has been tackled. However, the (offline) evaluation of tag recommendation algorithms usually suffers from difficulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores.
- A user reputation model for a user-interactive question answering systemhttps://puma.uni-kassel.de/bibtex/2858df3646b706ce6308a12cbf1585d58/jaeschkejaeschke2012-10-11T17:47:40+02:00answering collaborative question reputation search social web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Wei Chen" itemprop="url" href="/author/Wei%20Chen"><span itemprop="name">W. Chen</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Qingtian Zeng" itemprop="url" href="/author/Qingtian%20Zeng"><span itemprop="name">Q. Zeng</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Liu Wenyin" itemprop="url" href="/author/Liu%20Wenyin"><span itemprop="name">L. Wenyin</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Tianyong Hao" itemprop="url" href="/author/Tianyong%20Hao"><span itemprop="name">T. Hao</span></a></span>. </span><span itemtype="http://schema.org/PublicationIssue" itemscope="itemscope" itemprop="isPartOf"><span itemtype="http://schema.org/Periodical" itemscope="itemscope" itemprop="isPartOf"><span itemprop="name"><em>Concurrency and Computation: Practice and Experience</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">19 </span></span>(<span itemprop="issueNumber">15</span>):
<span itemprop="pagination">2091--2103</span></em> </span>(<em><span>2007<meta content="2007" itemprop="datePublished"/></span></em>)Thu Oct 11 17:47:40 CEST 2012Concurrency and Computation: Practice and Experience152091--2103A user reputation model for a user-interactive question answering system192007answering collaborative question reputation search social web In this paper, we propose a user reputation model and apply it to a user-interactive question answering system. It combines the social network analysis approach and the user rating approach. Social network analysis is applied to analyze the impact of participant users' relations to their reputations. User rating is used to acquire direct judgment of a user's reputation based on other users' experiences with this user. Preliminary experiments show that the computed reputations based on our proposed reputation model can reflect the actual reputations of the simulated roles and therefore can fit in well with our user-interactive question answering system. Copyright © 2006 John Wiley & Sons, Ltd.
- A Social Mechanism of Reputation Management in Electronic Communitieshttps://puma.uni-kassel.de/bibtex/2337afcb67138b927b27a9687199e8568/jaeschkejaeschke2012-10-11T17:46:32+02:00collaborative community management network quality reputation search social trust web <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bin Yu" itemprop="url" href="/author/Bin%20Yu"><span itemprop="name">B. Yu</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Munindar Singh" itemprop="url" href="/author/Munindar%20Singh"><span itemprop="name">M. Singh</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Cooperative Information Agents IV - The Future of Information Agents in Cyberspace</span>, </em><em>Volume 1860 von Lecture Notes in Computer Science, </em><em><span itemprop="publisher">Springer</span>, </em><em>Berlin/Heidelberg, </em></span>(<em><span>2000<meta content="2000" itemprop="datePublished"/></span></em>)Thu Oct 11 17:46:32 CEST 2012Berlin/HeidelbergCooperative Information Agents IV - The Future of Information Agents in Cyberspace355--393Lecture Notes in Computer ScienceA Social Mechanism of Reputation Management in Electronic Communities18602000collaborative community management network quality reputation search social trust web Trust is important wherever agents must interact. We consider the important case of interactions in electronic communities, where the agents assist and represent principal entities, such as people and businesses. We propose a social mechanism of reputation management, which aims at avoiding interaction with undesirable participants. Social mechanisms complement hard security techniques (such as passwords and digital certificates), which only guarantee that a party is authenticated and authorized, but do not ensure that it exercises its authorization in a way that is desirable to others. Social mechanisms are even more important when trusted third parties are not available. Our specific approach to reputation management leads to a decentralized society in which agents help each other weed out undesirable players.