JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2015, Vol. 50 ›› Issue (01): 31-36.doi: 10.6040/j.issn.1671-9352.3.2014.033
Previous Articles Next Articles
HUANG Chun-lan, WU Sheng-li
CLC Number:
[1] AGRAWAL R, GOLLAPUDI S, HALVERSON A, et al. Diversifying search results[C]// Proceedings of the 2nd ACM International Conference on Web Search and Data Mining. New York: ACM, 2009: 5-14. [2] CARBONELL J, GOLDSTEIN J. The use of MMR, diversity-based re-ranking for reordering documents and producing summaries[C]// Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 1998: 335-336. [3] WANG Jun, ZHU Jianhan. Portfolio theory of information retrieval[C]// Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2009: 115-122. [4] ZHAI Chengxiang, COHEN William W, LAFFERTY J. Beyond independent relevance: methods and evaluation metrics for subtopic retrieval[C]// Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2003: 10-17. [5] DANG Van, CROFT W B. Diversity by proportionality: an election-based approach to search result diversification[C]// Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2012: 65-74. [6] SANTOS R L T, MACDONALD C, OUNIS I. Exploiting query reformulations for web search result diversification[C]// Proceedings of the 19th International Conference on World Wide Web. New York: ACM, 2010: 881-890. [7] DANG V, CROFT B W. Term level search result diversification[C]// Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2013: 603-612. [8] AKTOLGA E, ALLAN J. Sentiment diversification with different biases[C]// Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2013: 593-602. [9] NGUYEN T N, KANHABUA N. Leveraging dynamic query subtopics for time-aware search result diversification[M]// Advances in Information Retrieval. New York: Springer International Publishing, 2014: 222-234. [10] YIN Xiaoshi, HUANG J X, LI Zhoujun, et al. A survival modeling approach to biomedical search result diversification using Wikipedia[J]. IEEE Transactions on Knowledge and Data Engineering, 2013, 25(6):1201-1212. [11] SAKAI T, DOU Zhicheng, YAMAMOTO T, et al. Summary of the NTCIR-10 INTENT-2 task: subtopic mining and search result diversification[C]// Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2013: 761-764. [12] ZHENG Wei, FANG Hui, YAO Conglei, et al. Leveraging integrated information to extract query subtopics for search result diversification[J]. Information Retrieval, 2014, 17(1):52-73. [13] ZHENG Wei, FANG Hui. A diagnostic study of search result diversification methods[C]// Proceedings of the 2013 Conference on the Theory of Information Retrieval. New York: ACM, 2013: 17. [14] LEE J H. Analyses of multiple evidence combination[C]// Proceedings of the 20th Annual International ACM SIGIR Conference. New York: ACM, 1997, 31(SI):267-276. [15] WU Shengli, BI Yaxin, ZENG Xiaoqin. The linear combination data fusion method in information retrieval[J]. Lecture Notes in Computer Science, 2011, 6861:219-233. [16] WU Shengli, MCCLEAN S. Performance prediction of data fusion for information retrieval[J]. Information Processing & Management, 2006, 42(4):899-915. [17] CLARKE C L A, CRASWELL N, SOBOROFF I, et al. Overview of the TREC 2011 web track[C]// Proceedings of TREC Conference. Gaithersburg:[s.n.]. 2011: 1-9. [18] CORMACK G V, CLARKE C L A, BUETTCHER S. Reciprocal rank fusion outperforms condorcet and individual rank learning methods[C]// Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2009: 758-759. [19] WU Shengli. Applying statistical principles to data fusion in information retrieval[J]. Expert Systems with Applications, 2009, 36(2):2997-3006. [20] KOHAVI R. A study of cross-validation and bootstrap for accuracy estimation and model selection[C]// Proceedings of the 14th International Joint Conference on Artificial Intelligence. San Mateo: Morgan Kaufmann Publishers, 1995: 1137-1143. |
[1] | SONG Yuan-zhang, LI Hong-yu, CHEN Yuan, WANG Jun-jie. P2P botnet detection method based on fractal and adaptive data fusion [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(3): 74-81. |
[2] | QIU Yu-feng, TANG Ji-hua*. Attribute inner fusion and data fusion mining#br# [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2014, 49(06): 11-17. |
[3] | CHEN Ke-rui, PAN Jun. Multi-source data fusion based on the expand vector space model [J]. J4, 2013, 48(11): 87-92. |
|