Simulation Studies of Different Dimensions of Users' Interests and their Impact on User Modeling and Information Filtering

  • Authors:

  • Javed Mostafa;Snehasis Mukhopadhyay;Mathew Palakal

  • Affiliations:

  • Laboratory of Applied Informatics Research, Indiana University, 030A, 1320 E 10th Ave., Bloomington, IN 47405-3907, USA. jm@indiana.edu;Department of Computer & Information Science, Indiana University Purdue University, 723 W. Michigan St., Indianapolis, IN 46202, USA;Department of Computer & Information Science, Indiana University Purdue University, 723 W. Michigan St., Indianapolis, IN 46202, USA

  • Venue:
  • Information Retrieval
  • Year:
  • 2003

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Abstract

Modeling users in information filtering systems is a difficult challenge due to dimensions such as nature, scope, and variability of interests. Numerous machine-learning approaches have been proposed for user modeling in filtering systems. The focus has been primarily on techniques for user model capture and representation, with relatively simple assumptions made about the type of users' interests. Although many studies claim to deal with “adaptive” techniques and thus they pay heed to the fact that different types of interests must be modeled or even changes in interests have to be captured, few studies have actually focused on the dynamic nature and the variability of user-interests and their impact on the modeling process. A simulation based information filtering environment called SIMSFITER was developed to overcome some of the barriers associated with conducting studies on user-oriented factors that can impact interests. SIMSIFTER implemented a user modeling approach known as reinforcement learning that has proven to be effective in previous filtering studies involving humans. This paper reports on several studies conducted using SIMSIFTER that examined the impact of key dimensions such as type of interests, rate of change of interests and level of user-involvement on modeling accuracy and ultimately on filtering effectiveness.