Empirical evaluation of explicit versus implicit acquisition of user profiles in information filtering systems

1999 ◽  
Author(s):  
Luz Marina Quiroga ◽  
Javed Mostafa
Keyword(s):  
Information Filtering ◽  
Empirical Evaluation ◽  
User Profiles

Methods and techniques for the evaluation of user-adaptive systems

2005 ◽  
Vol 20 (1) ◽  
pp. 1-37 ◽  
Author(s):  
CRISTINA GENA
Keyword(s):  
Information Retrieval ◽  
Grounded Theory ◽  
Human Computer Interaction ◽  
Adaptive Systems ◽  
State Of The Art ◽  
Information Filtering ◽  
Empirical Evaluation ◽  
Comprehensive Overview ◽  
Qualitative Approaches ◽  
Methods And Techniques

This article presents a comprehensive overview of methods and techniques used for the evaluation of user-adaptive systems. It describes the methodologies derived both from the evaluation of human–computer interaction systems and from information retrieval and information filtering systems by giving examples of the application of these methodologies in the user-adaptive systems. The state of the art and the main results in the evaluation of these systems are reported. In particular, empirical evaluation and layered approaches are discussed in detail. Finally, focus on less explored methodologies, such as qualitative approaches (e.g. Grounded Theory), is proposed.

HYPERTEXT BROWSING: A NEW MODEL FOR INFORMATION FILTERING BASED ON USER PROFILES AND DATA CLUSTERING

1996 ◽  
Vol 20 (1) ◽  
pp. 3-10
Author(s):  
Bracha Shapira ◽  
Peretz Shoval ◽  
Adi Raveh ◽  
Uri Hanani
Keyword(s):  
Data Clustering ◽  
Information Filtering ◽  
User Profiles ◽  
New Model

TEXT CATEGORIZATION IN AN INTELLIGENT AGENT FOR FILTERING INFORMATION ON THE WEB

2001 ◽  
Vol 15 (03) ◽  
pp. 527-549 ◽  
Author(s):  
G. L. GENTILI ◽  
M. MARINILLI ◽  
A. MICARELLI ◽  
F. SCIARRONE
Keyword(s):  
Confidence Interval ◽  
Text Categorization ◽  
Information Filtering ◽  
Empirical Evaluation ◽  
Intelligent Agent ◽  
Multilayer Perceptrons ◽  
Text Documents ◽  
Categorization System ◽  
The Web ◽  
Case Based

This paper presents a text categorization system, capable of analyzing HTML/text documents collected from the Web. The system is a component of a more extensive intelligent agent for adaptive information filtering on the Web. It is based on a hybrid case-based architecture, where two multilayer perceptrons are integrated into a case-based reasoner. An empirical evaluation of the system was performed by means of a confidence interval technique. The experimental results obtained are encouraging and support the choice of a hybrid case-based approach to text categorization.

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