<i>Implicit</i>: an agent-based recommendation system for web search

@inproceedings{birukou-et-al:aamas05, abstract = {The number of web pages available on Internet increases day after day, and consequently finding relevant information becomes more and more a hard task. However, when we consider communities of people with common interests, it is possible to improve the quality of the query results using knowledge extracted from the observed behaviors of the single users. In this paper we propose an agent-based recommendation system for supporting communities of people in searching the web by means of a popular search engine. Agents use data mining techniques in order to learn and discover users' behaviors, and they interact one another to share knowledge about their users. The paper presents also a set of experimental results showing, in terms of precision and recall, how agents interaction increases the performance of the overall system.}, address = {New York, NY, USA}, author = {Birukov, Alexander and Blanzieri, Enrico and Giorgini, Paolo }, booktitle = {AAMAS '05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems}, citeulike-article-id = {695963}, doi = {10.1145/1082473.1082567}, isbn = {1595930930}, keywords = {datamining, implicit\_culture, information\_retrieval, ir, mas, personalization, recommendation, web\_search}, pages = {618--624}, posted-at = {2006-06-14 16:56:31}, priority = {0}, publisher = {ACM Press}, title = {<i>Implicit</i>: an agent-based recommendation system for web search}, url = {http://portal.acm.org/citation.cfm?id=1082567}, year = {2005} }

TY - CONF ID - birukou-et-al:aamas05 L3 - citeulike-article-id:695963 TI - <i>Implicit</i>: an agent-based recommendation system for web search T2 - AAMAS '05: Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems SP - 618 EP - 624 AD - New York, NY, USA PB - ACM Press SN - 1595930930 N2 - The number of web pages available on Internet increases day after day, and consequently finding relevant information becomes more and more a hard task. However, when we consider communities of people with common interests, it is possible to improve the quality of the query results using knowledge extracted from the observed behaviors of the single users. In this paper we propose an agent-based recommendation system for supporting communities of people in searching the web by means of a popular search engine. Agents use data mining techniques in order to learn and discover users' behaviors, and they interact one another to share knowledge about their users. The paper presents also a set of experimental results showing, in terms of precision and recall, how agents interaction increases the performance of the overall system. KW - datamining KW - implicit_culture KW - information_retrieval KW - ir KW - mas KW - personalization KW - recommendation KW - web_search AU - Birukov, Alexander AU - Blanzieri, Enrico AU - Giorgini, Paolo PY - 2005/// UR - http://portal.acm.org/citation.cfm?id=1082567 ER -