Defining like-minded agents with the aid of visualization

ABSTRACT

Profile carrying agents offer the opportunity of meeting like minds and increasing efficiency in many information search applications. Profiles can also increase the sophistication of relationships and interactions in multi-agent systems in general. Such profiles or feature lists may be of the agent owner or of the information sought. Two key issues are choice of similarity measure and privacy of profiles. In this paper we adapt techniques used in information visualization and classification to address these two problems. The basic idea is to map the high dimensional profiles into a low dimensional space. The mapping is one-way, so that privacy is achieved. To compare mapped profiles in the low dimensional space, we discuss the choice of a number of similarity measures.

REFERENCES

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INDEX TERMS

Primary Classification:
  E. Data
  E.3 DATA ENCRYPTION

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Clustering; Search process

  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
        I.2.11 Distributed Artificial Intelligence
            Subjects: Multiagent systems
  I.5 PATTERN RECOGNITION
      I.5.3 Clustering
          Subjects: Similarity measures

General Terms:
Algorithms, Human Factors, Measurement, Security

Keywords:
matchmaking, personalization, profile privacy