Contact Point:
Giovanni Semeraro ( )
University of Bari, Italy
  

Technical Contact Points:
Pierpaolo Basile ( ) and Marco Degemmis ( )
  

Type of software:
PROTOTYPE
   

Descriptive Keywords:
Content-based Recommender System, User Profiling System
  

Potential Use and Applications:
The main target of personalization in a virtual information and knowledge environment is the reduction of information overload. User modeling and tracking activities provide the basis for a wide range of services that reuse the semantic information describing properties of the user. Such personalization services contribute to a more targeted information access. Typical applications of user profiles in an information environment are information filtering, personalized information recommendations, and targeted notification about changes in the information space. ITR system could be used for developing intelligent recommendation services in which items to be recommended are described by using text.
  

General Description:
ITem Recommender (ITR) is a content-based profiling system able to infer user profiles from textual documents rated by users according to their interests. User profiles are exploited in a recommendation process to filter and to suggest new documents to the users.
  
The system implements a naïve Bayes text categorization algorithm and it is able to classify documents as interesting or uninteresting for a specific user by exploiting a probabilistic model learned from training examples, the documents rated by that user in the past. ITR represents documents by using senses corresponding to concepts identified from words in the original text through an automated Word Sense Disambiguation procedure. The final outcome of the learning process is a probabilistic model of the user interests. The model is used as a personal profile including those concepts that turn out to be most indicative of the user’s preferences, according to the value of the parameters of the model.
   

Technical Description: 
The system is implemented in Java.
Database: MySQL ver. 4.1 or later (© 1995-2007 MySQL AB. All rights reserved)
Web Server: Apache Tomcat ver. 5.0 or later (Copyright © 1999-2006, The Apache Software Foundation)
Other resources: WordNet 2.0 (WordNet 2.0 Copyright 2003 by Princeton University. All rights reserved)
   

Required User Skills:
Java programming.
   

Pre-Requisites for Installation:
Java Virtual Machine 1.5.0 or later
Apache Tomcat 5.0 or later
MySQL 4.1 or later
WordNet 2.0
  
ITR has been compiled and tested on JDK 1.5.0 from SUN, and should work on any compliant Virtual Machine. Currently running under Linux/Windows.
  

Conditions of Use:
Contact the authors.