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Yu, Weiren and McCann, Julie Ann (2015) High quality graph-based similarity search. In: SIGIR '15. Published in: SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval pp. 83-92. doi:10.1145/2766462.2767720
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WRAP-high-quality-graph-based-similiarity-Yu-.pdf - Accepted Version - Requires a PDF viewer. Download (1982Kb) | Preview |
Official URL: http://dx.doi.org/10.1145/2766462.2767720
Abstract
SimRank is an influential link-based similarity measure that has been used in many fields of Web search and sociometry. The best-of-breed method by Kusumoto et. al., however, does not always deliver high-quality results, since it fails to accurately obtain its diagonal correction matrix D. Besides, SimRank is also limited by an unwanted "connectivity trait": increasing the number of paths between nodes a and b often incurs a decrease in score s(a,b). The best-known solution, SimRank++, cannot resolve this problem, since a revised score will be zero if a and b have no common in-neighbors. In this paper, we consider high-quality similarity search. Our scheme, SR#, is efficient and semantically meaningful: (1) We first formulate the exact D, and devise a "varied-D" method to accurately compute SimRank in linear memory. Moreover, by grouping computation, we also reduce the time of from quadratic to linear in the number of iterations. (2) We design a "kernel-based" model to improve the quality of SimRank, and circumvent the "connectivity trait" issue. (3) We give mathematical insights to the semantic difference between SimRank and its variant, and correct an argument: "if D is replaced by a scaled identity matrix, top-K rankings will not be affected much". The experiments confirm that SR# can accurately extract high-quality scores, and is much faster than the state-of-the-art competitors.
| Item Type: | Conference Item (Paper) | ||||
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| Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software | ||||
| Divisions: | Faculty of Science > Computer Science | ||||
| Library of Congress Subject Headings (LCSH): | Graph theory -- Data processing, Link theory, Graph algorithms, Electronic information resource searching | ||||
| Journal or Publication Title: | SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval | ||||
| Publisher: | ACM | ||||
| Book Title: | Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '15 | ||||
| Official Date: | August 2015 | ||||
| Dates: |
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| Page Range: | pp. 83-92 | ||||
| DOI: | 10.1145/2766462.2767720 | ||||
| Status: | Peer Reviewed | ||||
| Publication Status: | Published | ||||
| Publisher Statement: | © Author | ACM 2015 This is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in SIGIR '15: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval http://dx.doi.org/10.1145/2766462.2767720 | ||||
| Access rights to Published version: | Restricted or Subscription Access | ||||
| Conference Paper Type: | Paper | ||||
| Title of Event: | SIGIR '15 | ||||
| Type of Event: | Conference |
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