Implicit: an agent-based recommendation system for web search

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. Categories and Subject Descriptors H.3.3 [Information Storage and Retrieval]: Information Search and Retrieval—information filtering, relevance feedback,search...

• Type: Conference
• Year: 2005
• Where: ATAL
• Authors: Aliaksandr Birukou, Enrico Blanzieri, Paolo Giorgini
• Keywords: ATAL 2005, Data Mining, Data Mining Techniques, Recommendation System