<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Merritt, David</style></author><author><style face="normal" font="default" size="100%">Hung, Pei-Yao</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Treem, Jeffrey W.</style></author><author><style face="normal" font="default" size="100%">Leonardi, Paul M.</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Expertise Finding: A Socio-Technical Design Space Analysis</style></title><secondary-title><style face="normal" font="default" size="100%">Expertise, Communication, and Organizing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">MISSING_URL_ABSTRACT</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Oxford University</style></publisher><pub-location><style face="normal" font="default" size="100%">New York</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Juri Dachtera</style></author><author><style face="normal" font="default" size="100%">Pipek, Volkmar</style></author><author><style face="normal" font="default" size="100%">Wulf, Volker</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Sharing Knowledge and Expertise: The CSCW View of Knowledge Management</style></title><secondary-title><style face="normal" font="default" size="100%">Computer Supported Cooperative Work (CSCW) Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">collective intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">cscw</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">information access</style></keyword><keyword><style  face="normal" font="default" size="100%">knowledge sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">QA</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2013</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">531-573</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Knowledge Management (KM) is a diffuse and controversial term, which has been used by a large number of research disciplines. CSCW, over the last 20 years, has taken a critical stance towards most of these approaches, and instead, CSCW shifted the focus towards a practice-based perspective. This paper surveys CSCW researchers’ viewpoints on what has become called ‘knowledge sharing’ and ‘expertise sharing’. These are based in an understanding of the social contexts of knowledge work and practices, as well as in an emphasis on communication among knowledgeable humans. The paper provides a summary and overview of the two strands of knowledge and expertise sharing in CSCW, which, from an analytical standpoint, roughly represent ’generations’ of research: an ’object-centric’ and a ’people-centric’ view. We also survey the challenges and opportunities ahead.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Kevin K. Nam</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Lada A. Adamic</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Questions in, Knowledge iN? A study of Naver’s Question Answering Community</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Conference on Human Factors in Computing Systems (CHI’09)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">collective help</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">information access</style></keyword><keyword><style  face="normal" font="default" size="100%">online communities</style></keyword><keyword><style  face="normal" font="default" size="100%">Q&amp;A communities</style></keyword><keyword><style  face="normal" font="default" size="100%">QA</style></keyword><keyword><style  face="normal" font="default" size="100%">question-answering</style></keyword><keyword><style  face="normal" font="default" size="100%">social computing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Large general-purposed community question-answering sites are becoming popular as a new venue for generating knowledge and helping users in their information needs. In this paper we analyze the characteristics of knowledge generation and user participation behavior in the largest question-answering online community in South Korea, Naver Knowledge-iN. We collected and analyzed over 2.6 million question/answer pairs from fifteen categories between 2002 and 2007, and have interviewed twenty six users to gain insights into their motivations,roles, usage and expertise. We find altruism, learning, and competency are frequent motivations for top answerers to participate, but that participation is often highly intermittent. Using a simple measure of user performance, we find that higher levels of participation correlate with better performance. We also observe that users are motivated in part through a point system to build a comprehensive knowledge database. These and other insights have significant implications for future knowledge generating online communities.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Lada A. Adamic</style></author><author><style face="normal" font="default" size="100%">Zhang, Jun</style></author><author><style face="normal" font="default" size="100%">Bakshy, Eytan</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Knowledge Sharing and Yahoo Answers: Everyone Knows Something</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 17th International Conference on World Wide Web</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">help seeking</style></keyword><keyword><style  face="normal" font="default" size="100%">knowledge sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">online communities</style></keyword><keyword><style  face="normal" font="default" size="100%">Q&amp;A communities</style></keyword><keyword><style  face="normal" font="default" size="100%">QA communities</style></keyword><keyword><style  face="normal" font="default" size="100%">question answering</style></keyword><keyword><style  face="normal" font="default" size="100%">social network analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Yahoo Answers (YA) is a large and diverse question-answer forum, acting not only as a medium for sharing technical knowledge, but as a place where one can seek advice, gather opinions, and satisfy one&#039;s curiosity about a countless number of things. In this paper, we seek to understand YA&#039;s knowledge sharing and activity. We analyze the forum categories and cluster them according to content characteristics and patterns of interaction among the users. While interactions in some categories resemble expertise sharing forums, others incorporate discussion, everyday advice, and support. With such a diversity of categories in which one can participate, we find that some users focus narrowly on specific topics, while others participate across categories. This not only allows us to map related categories, but to characterize the entropy of the users&#039; interests. We find that lower entropy correlates with receiving higher answer ratings, but only for categories where factual expertise is primarily sought after. We combine both user attributes and answer characteristics to predict, within a given category, whether a particular answer will be chosen as the best answer by the asker.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang, Jun</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Lada A. Adamic</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expertise networks in online communities: structure and algorithms</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 16th international conference on World Wide Web (WWW&#039;07)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">expert locators</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">help seeking</style></keyword><keyword><style  face="normal" font="default" size="100%">online communities</style></keyword><keyword><style  face="normal" font="default" size="100%">simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">social network analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">ACM</style></publisher><pages><style face="normal" font="default" size="100%">221–230</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Web-based communities have become important places for people to seek and share expertise. We find that networks in these communities typically differ in their topology from other online networks such as the World Wide Web. Systems targeted to augment web-based communities by automatically identifying users with expertise, for example, need to adapt to the underlying interaction dynamics. In this study, we analyze the Java Forum, a large online help-seeking community, using social network analysis methods. We test a set of network-based ranking algorithms, including PageRank and HITS, on this large size social network in order to identify users with high expertise. We then use simulations to identify a small number of simple simulation rules governing the question-answer dynamic in the network. These simple rules not only replicate the structural characteristics and algorithm performance on the empirically observed Java Forum, but also allow us to evaluate how other algorithms may perform in communities with different characteristics. We believe this approach will be fruitful for practical algorithm design and implementation for online expertise-sharing communities.&lt;br&gt;&amp;nbsp;&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang, Jun</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Lada A. Adamic</style></author><author><style face="normal" font="default" size="100%">Kevin K. Nam</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">QuME: A Mechanism to Support Expertise Finding in Online Help-seeking Communities</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 20th Annual ACM Symposium on User Interface Software and Technology (UIST&#039;07)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cscw</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise location</style></keyword><keyword><style  face="normal" font="default" size="100%">social networks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">10/2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">111–114</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Help-seeking communities have been playing an increasingly critical role in the way people seek and share information. However, traditional help-seeking mechanisms of these online communities have some limitations. In this paper, we describe an expertise-finding mechanism that attempts to alleviate the limitations caused by not knowing users&#039; expertise levels. As a result of using social network data from the online community, this mechanism can automatically infer expertise level. This allows, for example, a question list to be personalized to the user&#039;s expertise level as well as to keyword similarity. We believe this expertise location mechanism will facilitate the development of next generation help-seeking communities.&lt;/p&gt;
</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zhang, Jun</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Searching for Expertise in Social Networks: A Simulation of Potential Strategies</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 2005 International ACM SIGGROUP Conference on Supporting Group Work (Group&#039;05)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise location</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">information seeking</style></keyword><keyword><style  face="normal" font="default" size="100%">organizational simulations</style></keyword><keyword><style  face="normal" font="default" size="100%">social computing</style></keyword><keyword><style  face="normal" font="default" size="100%">social networks</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">71–80</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;People search for people with suitable expertise all of the time in their social networks - to answer questions or provide help. Recently, efforts have been made to augment this searching. However, relatively little is known about the social characteristics of various algorithms that might be useful. In this paper, we examine three families of searching strategies that we believe may be useful in expertise location. We do so through a simulation, based on the Enron email data set. (We would be unable to suitably experiment in a real organization, thus our need for a simulation.) Our emphasis is not on graph theoretical concerns, but on the social characteristics involved. The goal is to understand the tradeoffs involved in the design of social network based searching engines.&lt;br&gt;&amp;nbsp;&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">McDonald, David W.</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expertise Recommender: A Flexible Recommendation System and Architecture</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 2000 ACM Conference on Computer Supported Cooperative Work (CSCW&#039;00)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">collaborative filtering</style></keyword><keyword><style  face="normal" font="default" size="100%">expert locators</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise location</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">information seeking</style></keyword><keyword><style  face="normal" font="default" size="100%">recommendation systems</style></keyword><keyword><style  face="normal" font="default" size="100%">software architecture</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">231–240</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Locating the expertise necessary to solve difficult problems is a nuanced social and collaborative problem. In organizations, some people assist others in locating expertise by making referrals. People who make referrals fill key organizational roles that have been identified by CSCW and affiliated research. Expertise locating systems are not designed to replace people who fill these key organizational roles. Instead, expertise locating systems attempt to decrease workload and support people who have no other options. Recommendation systems are collaborative software that can be applied to expertise locating. This work describes a general recommendation architecture that is grounded in a field study of expertise locating. Our expertise recommendation system details the work necessary to fit expertise recommendation to a work setting. The architecture and implementation begin to tease apart the technical aspects of providing good recommendations from social and collaborative concerns.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">McDonald, David W.</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Just Talk to Me: A Field Study of Expertise Location</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 1998 ACM Conference on Computer Supported Cooperative Work (CSCW&#039;98)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bug reporting</style></keyword><keyword><style  face="normal" font="default" size="100%">CMC</style></keyword><keyword><style  face="normal" font="default" size="100%">computer mediated communications</style></keyword><keyword><style  face="normal" font="default" size="100%">expert locators</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise location</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise networks</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">information seeking</style></keyword><keyword><style  face="normal" font="default" size="100%">knowledge networks</style></keyword><keyword><style  face="normal" font="default" size="100%">knowledge sharing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year><pub-dates><date><style  face="normal" font="default" size="100%">11/1998</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">315–324</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Everyday, people in organizations must solve their problems to get their work accomplished. To do so, they often must find others with knowledge and information. Systems that assist users with finding such expertise are increasingly interesting to organizations and scientific communities. But, as we begin to design and construct such systems, it is important to determine what we are attempting to augment. Accordingly, we conducted a five-month field study of a medium-sized software firm. We found the participants use complex, iterative behaviors to minimize the number of possible expertise sources, while at the same time, provide a high possibility of garnering the necessary expertise. We briefly consider the design implications of the identification, selection, and escalation behaviors found during our field study.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Starr, Brian</style></author><author><style face="normal" font="default" size="100%">Pazzani, Michael</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The Do-I-Care Agent: Effective Social Discovery and Filtering on the Web</style></title><secondary-title><style face="normal" font="default" size="100%">Computer-Assisted Information Searching on Internet (RIAO&#039;97)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">agents</style></keyword><keyword><style  face="normal" font="default" size="100%">collaboration</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">social filtering</style></keyword><keyword><style  face="normal" font="default" size="100%">World Wide Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1997</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">17–31</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The Web is a vast, dynamic source of information and resources. Because of its size and diversity, it is increasingly likely that if the information one seeks is not already there, it will be soon. Unfortunately, finding the right places to look, and persistently revisiting those places until the information is available is an onerous task. In this paper, we describe Do-I-Care (DICA), an agent that uses both technical and social mechanisms to ease the burden of locating &quot;interesting&quot; new information and resources on the Web.&lt;/p&gt;&lt;p&gt;DICA monitors Web pages previously found by the agent&#039;s user to be relevant for any changes. It then compares these changes against a user model, classifies them as potentially interesting or not, and reports the interesting changes to the user. The user model is derived by accepting relevance feedback on changes previously found. Because the agent focuses on changes to known pages rather than discovering new pages, we increase the likelihood that the information found will be interesting.&lt;/p&gt;&lt;p&gt;DICA combines an effortless collaboration mechanism with the natural incentives for individual users to maintain and train their own agents. Simply by pointing DICA agents at other agents, changes and opinions can be propagated from agent to agent automatically. Thus, individuals train and use DICA for themselves, but by using a simple technical mechanism, other users can use those results without the additional effort that often accompanies collaboration.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Starr, Brian</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Pazzani, Michael</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Do I Care?—Tell me what’s changed on the Web</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the AAAI Spring Symposium on Machine Learning in Information Access</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">agents</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">social search</style></keyword><keyword><style  face="normal" font="default" size="100%">World Wide Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">119-121</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;We describe the Do-I-Care agent, which uses machine learning to detect &quot;interesting&quot; changes to Web pages previously found to be relevant. Because this agent focuses on changes to known pages rather than discovering new pages, we increase the likelihood that the information found will be interesting. The agent’s accuracy in finding interesting changes and in learning is improved by exploiting regularities in how pages are changed. Additionally, these agents can be used collaboratively by cascading them and by propagating interesting findings to other users’ agents.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Starr, Brian</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Pazzani, Michael</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Do-I-Care: A Collaborative Web Agent</style></title><secondary-title><style face="normal" font="default" size="100%">Conference on Human Factors in Computing Systems (CHI&quot;96)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">agents</style></keyword><keyword><style  face="normal" font="default" size="100%">collaboration</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise sharing</style></keyword><keyword><style  face="normal" font="default" size="100%">machine learning</style></keyword><keyword><style  face="normal" font="default" size="100%">social filtering</style></keyword><keyword><style  face="normal" font="default" size="100%">social search</style></keyword><keyword><style  face="normal" font="default" size="100%">World Wide Web</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">Complete</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">v.2, 273–274</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Social filtering and collaborative resource discovery mechanisms often fail because of the extra burden, even tiny, placed on the user. This work proposes an innovative World Wide Web agent that uses a model of collaboration that leverages the natural incentives for individual users to easily provide for collaborative work.&lt;/p&gt;</style></abstract></record></records></xml>