Dynamically constructing user profiles with similarity-based online incremental clustering

Roman Y. Shtykh, Qun Jin

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

User profiling is a widely used technique to analyse and store user interests and preferences to apply this knowledge to improve user experiences with information systems. In this research paper, we present an approach for dynamically constructing user profiles, particularly from uniform relevance feedback in information-seeking activities. We propose an inference method for user interests, which we call High-Similarity Sequence Data-Driven (H2S2D) clustering and discuss its peculiarities and show its superiority for the creation of high-quality concepts, which are the elementary constituents of user profiles. To reflect the volatility of user interests and emphasise the steadiness of persistent preferences, we adopt recency, frequency and persistency as the three main criteria for multi-layered dynamic profile construction and update.

Original languageEnglish
Pages (from-to)377-397
Number of pages21
JournalInternational Journal of Advanced Intelligence Paradigms
Volume1
Issue number4
DOIs
Publication statusPublished - 2009 Jun 1

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Keywords

  • High-similarity sequence data-driven clustering
  • Interest dynamics
  • Multi-layered user profile
  • Relevance feedback
  • User model

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)
  • Applied Mathematics

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