Cited by 1,567Information Retrieval and Search BehaviorWeb Data Mining and AnalysisRecommender Systems and Techniques

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Topic-sensitive PageRank

Haveliwala, Taher H.·2002·Proceedings of the 11th international conference on World Wide Web

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Abstract

In the original PageRank algorithm for improving the ranking of search-query results, a single PageRank vector is computed, using the link structure of the Web, to capture the relative "importance" of Web pages, independent of any particular search query. To yield more accurate search results, we propose computing a set of PageRank vectors, biased using a set of representative topics, to capture more accurately the notion of importance with respect to a particular topic. By using these (precomputed) biased PageRank vectors to generate query-specific importance scores for pages at query time, we show that we can generate more accurate rankings than with a single, generic PageRank vector. For ordinary keyword search queries, we compute the topic-sensitive PageRank scores for pages satisfying the query using the topic of the query keywords. For searches done in context (e.g., when the search query is performed by highlighting words in a Web page), we compute the topic-sensitive PageRank scores using the topic of the context in which the query appeared.

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Authors: Haveliwala, T. H.
Affiliations: Stanford University (US)
Corresponding author: Haveliwala, Taher H.
Venue: Proceedings of the 11th international conference on World Wide Web
Publisher: ACM
Published: 2002-05-07
Pages: 517-526
DOI: 10.1145/511446.511513
OpenAlex: W2170344111
Cited by: 1,567
References: 15
Citations by year: 2026: 32 · 2025: 22 · 2024: 17 · 2023: 32 · 2022: 21 · 2021: 44 · 2020: 54 · 2019: 67 · 2018: 79 · 2017: 81 · 2016: 87 · 2015: 79 · 2014: 97 · 2013: 78 · 2012: 93
FWCI: 44.04
Type: conference paper
Language: en
Open access: closed
Licence: www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations: International World Wide Web Conference (WWW) (publishedVersion)
Topics: Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
Physical Sciences › Computer Science › Information Systems › Web Data Mining and Analysis
Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
Keywords: PageRank, web page ranking, search result ranking, context-aware retrieval, web link structure

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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Topic-sensitive PageRank

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Citations

Cites

Cited by

Related

Metadata

Record

About these checks