Self-adaptive user profiles for large-scale data delivery

Ugur Cetintemel, Michael J. Franklin, C. Lee Giles

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

Abstract

Push-based data delivery requires knowledge of user interests for making scheduling, bandwidth allocation, and routing decisions. Such information is maintained as user profiles. We propose a new incremental algorithm for constructing user profiles based on monitoring and user feedback. In contrast to earlier approaches, which typically represent profiles as a single weighted interest vector, we represent user profiles as multiple interest vectors, whose number, size, and elements change adaptively based on user access behavior. This flexible approach allows the profile to more accurately represent complex user interests. Although there has been significant research on user profiles, our approach is unique its that it can be tuned to trade off profile complexity and quality. This feature, together with its incremental nature, makes our method suitable for use in large-scale information filtering applications such as push-based WWW page dissemination. We evaluate the method by experimentally investigating its ability to categorize WWW pages taken from Yahoo! categories. Our results show that the method can provide high filtering effectiveness with modest profile sizes and can effectively adapt to changes in users' interests.

Original languageEnglish (US)
Pages (from-to)622-633
Number of pages12
JournalProceedings - International Conference on Data Engineering
StatePublished - 2000

All Science Journal Classification (ASJC) codes

  • Software
  • Engineering(all)
  • Engineering (miscellaneous)

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