The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Topic-sensitive PageRank
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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The anatomy of a large-scale hypertextual Web search engine
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Research works printed but not linked: 6
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NERank: Bringing Order to Named Entities from Texts
Wang, C., Zhang, R., He, X., Zhou, G., & Zhou, A. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-45814-4_2 · Cited by 4
Traffic Risk Mining Using Partially Ordered Non-Negative Matrix Factorization
Lee, T., Matsushima, S., & Yamanishi, K. (2016) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2016.71 · Cited by 4
From RAG to Memory: Non-Parametric Continual Learning for Large Language Models
Gutiérrez, B. J., Shu, Y., Qi, W., Zhou, S., & Su, Y. (2025) · arXiv · doi:10.48550/arxiv.2502.14802 · Cited by 2
“To address the associativity gap, the authors of HippoRAG (Guti´errez et al., 2024) use the Personalized PageRank algorithm (Haveliwala, 2002) and an LLM’s ability to automatically construct a KG and endow the retrieval process with multi-hop reasoning capabilities.”
(2003) · Cited by 312
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Metadata
- 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
- 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
- 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
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