Conference proceeding
Improving web search results using affinity graph
Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, pp.504-511
SIGIR '05
08/15/2005
DOI: 10.1145/1076034.1076120
Abstract
In this paper, we propose a novel ranking scheme named Affinity Ranking (AR) to re-rank search results by optimizing two metrics: (1) diversity -- which indicates the variance of topics in a group of documents; (2) information richness -- which measures the coverage of a single document to its topic. Both of the two metrics are calculated from a directed link graph named Affinity Graph (AG). AG models the structure of a group of documents based on the asymmetric content similarities between each pair of documents. Experimental results in Yahoo! Directory, ODP Data, and Newsgroup data demonstrate that our proposed ranking algorithm significantly improves the search performance. Specifically, the algorithm achieves 31% improvement in diversity and 12% improvement in information richness relatively within the top 10 search results.
Details
- Title: Subtitle
- Improving web search results using affinity graph
- Creators
- Benyu Zhang - Microsoft Research AsiaHua Li - Peking UniversityYi Liu - Michigan State UniversityLei Ji - Beijing Institute of TechnologyWensi Xi - Virginia TechWeiguo Fan - Virginia TechZheng Chen - Microsoft Research AsiaWei-Ying Ma - Microsoft Research Asia
- Resource Type
- Conference proceeding
- Publication Details
- Proceedings of the 28th annual international ACM SIGIR conference on research and development in information retrieval, pp.504-511
- Publisher
- ACM
- Series
- SIGIR '05
- DOI
- 10.1145/1076034.1076120
- Language
- English
- Date published
- 08/15/2005
- Academic Unit
- Business Analytics
- Record Identifier
- 9984380481502771
Metrics
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