PUMA publications for /user/jaeschke/agenthttps://puma.uni-kassel.de/user/jaeschke/agentPUMA RSS feed for /user/jaeschke/agent2024-03-29T02:03:52+01:00A Taxonomy of Recommender Agents on the Internethttps://puma.uni-kassel.de/bibtex/2f713e3f6acc112d9fbfd10216589d7db/jaeschkejaeschke2012-12-10T09:57:34+01:00agent recommender survey <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Miquel Montaner" itemprop="url" href="/author/Miquel%20Montaner"><span itemprop="name">M. Montaner</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Beatriz López" itemprop="url" href="/author/Beatriz%20L%c3%b3pez"><span itemprop="name">B. López</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Josep Lluís de la Rosa" itemprop="url" href="/author/Josep%20Llu%c3%ads%20de%20la%20Rosa"><span itemprop="name">J. de la Rosa</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>Artificial Intelligence Review</em></span></span> <em><span itemtype="http://schema.org/PublicationVolume" itemscope="itemscope" itemprop="isPartOf"><span itemprop="volumeNumber">19 </span></span>(<span itemprop="issueNumber">4</span>):
<span itemprop="pagination">285--330</span></em> </span>(<em><span>2003<meta content="2003" itemprop="datePublished"/></span></em>)Mon Dec 10 09:57:34 CET 2012Artificial Intelligence Review4285--330A Taxonomy of Recommender Agents on the Internet192003agent recommender survey Recently, Artificial Intelligence techniques have proved useful inhelping users to handle the large amount of information on the Internet.The idea of personalized search engines, intelligent software agents,and recommender systems has been widely accepted among users who requireassistance in searching, sorting, classifying, filtering and sharingthis vast quantity of information. In this paper, we present astate-of-the-art taxonomy of intelligent recommender agents on theInternet. We have analyzed 37 different systems and their references andhave sorted them into a list of 8 basic dimensions. These dimensions arethen used to establish a taxonomy under which the systems analyzed areclassified. Finally, we conclude this paper with a cross-dimensionalanalysis with the aim of providing a starting point for researchers toconstruct their own recommender system.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.