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A Taxonomy of Recommender Agents on theInternet

Published: 01 June 2003 Publication History

Abstract

Recently, Artificial Intelligence techniques have proved useful in helping users to handle the large amount of information on the Internet. The idea of personalized search engines, intelligent software agents, and recommender systems has been widely accepted among users who require assistance in searching, sorting, classifying, filtering and sharing this vast quantity of information. In this paper, we present a state-of-the-art taxonomy of intelligent recommender agents on the Internet. We have analyzed 37 different systems and their references and have sorted them into a list of 8 basic dimensions. These dimensions are then used to establish a taxonomy under which the systems analyzed are classified. Finally, we conclude this paper with a cross-dimensional analysis with the aim of providing a starting point for researchers to construct their own recommender system.

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M. Sasikumar

As the amount of information accessible on the Internet continues to grow steadily, intelligent software agents are becoming critical intermediaries for effective access to this information repertoire. The challenges of implementing such systems include handling the large volume of data, formulating proper profile representations, and the time-varying nature of user profiles. This paper studies 37 recommender agents with respect to their area of application, as well as the techniques they employ. On the techniques side, the paper identifies eight major aspects for comparison, broadly categorized into the management and exploitation of user profiles. Under management, there are five themes, namely, profile representation, generation of initial profile, the source for obtaining relevance feedback, profile learning technique, and profile adaptation techniques. The core of recommender systems is information filtering. Three main models are used for this component: demographic filtering, content-based filtering, and collaborative filtering. The filtering method constitutes the sixth aspect. Methods for profile-item matching and profile-profile matching are the last two aspects. Under each of these aspects, techniques used by the various agents selected for study are briefly discussed and tabulated. A brief comparative analysis of the various approaches is also presented in most cases. At the end of the paper, in a cross-dimensional analysis, the various aspects are contrasted against the domain of application deriving trends, such as "collaborative filtering is predominant in item recommendation domains." The paper is well structured, and the level of detail is just right in most places. A little more detail on major topics, such as the various filtering methods, would attract a wider readership. There is a good set of references (nearly 100) for more detailed investigations. On the whole, this is an excellent survey paper. Online Computing Reviews Service

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Published In

cover image Artificial Intelligence Review
Artificial Intelligence Review  Volume 19, Issue 4
June 2003
67 pages

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Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2003

Author Tags

  1. agents
  2. information filtering
  3. personalization
  4. profile exploitation
  5. profile generation
  6. profile maintenance
  7. recommender systems
  8. taxonomy
  9. user modelling

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