<?xml version="1.0" encoding="UTF-8"?><xml><records><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>