Network flow for collaborative ranking

Ziming Zhuang, Silviu Cucerzan, C. Lee Giles

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

1 Scopus citations

Abstract

In query based Web search, a significant percentage of user queries are underspecified, most likely by naive users. Collaborative ranking helps the naive user by exploiting the collective expertise. We present a novel algorithmic model inspired by the network flow theory, which constructs a search network based on search engine logs to describe the relationship between the relevant entities in search: queries, documents, and users. This formal model permits the theoretical investigation of the nature of collaborative ranking in more concrete terms, and the learning of the dependence relations among the different entities. FlowRank, an algorithm derived from this model through an analysis of empirical usage patterns, is implemented and evaluated. We empirically show its potential in experiments involving real-world user relevance ratings and a random sample of 1,334 documents and 100 queries from a popular document search engine. Definite improvements over two baseline ranking algorithms for approximately 47% of the queries are reported.

Original languageEnglish (US)
Title of host publicationKnowledge Discovery in Databases
Subtitle of host publicationPKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings
PublisherSpringer Verlag
Pages434-445
Number of pages12
ISBN (Print)3540453741, 9783540453741
StatePublished - Jan 1 2006
Event10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006 - Berlin, Germany
Duration: Sep 18 2006Sep 22 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4213 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other10th European Conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2006
CountryGermany
CityBerlin
Period9/18/069/22/06

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhuang, Z., Cucerzan, S., & Giles, C. L. (2006). Network flow for collaborative ranking. In Knowledge Discovery in Databases: PKDD 2006 - 10th European Conference on Principles and Practice of Knowledge Discovery in Databases, Proceedings (pp. 434-445). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4213 LNAI). Springer Verlag.