The Web has grown from a simple hypertext system for research labs to an ubiquitous information system including virtually all human knowledge, e.g., movies, images, music, documents, etc. The traditional browsing activity seems to be often inadequate to locate information satisfying the user needs. Even search engines, based on the Information Retrieval approach, with their huge indexes show many drawbacks, which force users to sift through long lists of results or reformulate queries several times. Recently, an important research activity effort has been focusing on this vast amount of machine-accessible knowledge and on how it can be exploited in order to match the user needs. The personalization and adaptation of the human-computer interaction in information seeking by means of machine learning techniques and in AI-based representations of the information help users to address the overload problem. This chapter illustrates the most important approaches proposed to personalize the access to information, in terms of gathering resources related to given topics of interest and ranking them as a function of the current user needs and activities, as well as examples of prototypes and Web systems.
ALESSANDRO MICARELLI, FABIO GASPARETTI, & CLAUDIO BIANCALANA (2006). Intelligent Search on the Internet. In Reasoning, Action and Interaction in AI Theories and Systems (pp. 247-264).
|Titolo:||Intelligent Search on the Internet|
|Data di pubblicazione:||2006|
|Citazione:||ALESSANDRO MICARELLI, FABIO GASPARETTI, & CLAUDIO BIANCALANA (2006). Intelligent Search on the Internet. In Reasoning, Action and Interaction in AI Theories and Systems (pp. 247-264).|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|