Inferring user interests from relevance feedback with high similarity sequence data-driven clustering

Roman Y. Shtykh, Qun Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Relevance feedback is an important source of information about a user and often used for usage and user modeling for further personalization of usersystem interactions. In this paper we present a method to infer the user's interests from his/her relevance feedback using an online incremental clustering method. For inference of a new interest (concept) and concept update the method uses the similarity characteristics of uniform user relevance feedback. It is fast, easy to implement and gives reasonable clustering results. We evaluate the method against two different data sets, demonstrate and discuss the outcomes.

Original languageEnglish
Title of host publicationProceedings of the 2nd International Symposium on Universal Communication, ISUC 2008
Pages390-396
Number of pages7
DOIs
Publication statusPublished - 2008 Dec 1
Event2nd International Symposium on Universal Communication, ISUC 2008 - Osaka, Japan
Duration: 2008 Dec 152008 Dec 16

Publication series

NameProceedings of the 2nd International Symposium on Universal Communication, ISUC 2008

Conference

Conference2nd International Symposium on Universal Communication, ISUC 2008
CountryJapan
CityOsaka
Period08/12/1508/12/16

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ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Software

Cite this

Shtykh, R. Y., & Jin, Q. (2008). Inferring user interests from relevance feedback with high similarity sequence data-driven clustering. In Proceedings of the 2nd International Symposium on Universal Communication, ISUC 2008 (pp. 390-396). [4724491] (Proceedings of the 2nd International Symposium on Universal Communication, ISUC 2008). https://doi.org/10.1109/ISUC.2008.39