TY - GEN
T1 - Improving web search results using affinity graph
AU - Zhang, Benyu
AU - Li, Hua
AU - Liu, Yi
AU - Ji, Lei
AU - Xi, Wensi
AU - Fan, Weiguo
AU - Chen, Zheng
AU - Ma, Wei-Ying
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2005
Y1 - 2005
N2 - 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. © 2005 ACM.
AB - 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. © 2005 ACM.
KW - affinity ranking
KW - diversity
KW - information retrieval
KW - information richness
KW - link analysis
UR - http://www.scopus.com/inward/record.url?scp=84885587114&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84885587114&origin=recordpage
U2 - 10.1145/1076034.1076120
DO - 10.1145/1076034.1076120
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1595930345
SN - 9781595930347
T3 - SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 504
EP - 511
BT - SIGIR 2005 - Proceedings of the 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval
T2 - 28th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2005
Y2 - 15 August 2005 through 19 August 2005
ER -