請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52999完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 鄭卜壬(Pu-Jen Cheng) | |
| dc.contributor.author | Jyun-Yu Jiang | en |
| dc.contributor.author | 姜俊宇 | zh_TW |
| dc.date.accessioned | 2021-06-15T16:38:27Z | - |
| dc.date.available | 2017-08-17 | |
| dc.date.copyright | 2015-08-17 | |
| dc.date.issued | 2015 | |
| dc.date.submitted | 2015-08-12 | |
| dc.identifier.citation | [1] P. N. Bennett, D. M. Chickering, and A. Mityagin. Learning consensus opinion:
mining data from a labeling game. In Proc. of WWW, pages 121–130. ACM, 2009. [2] P. N. Bennett, F. Radlinski, R. W. White, and E. Yilmaz. Inferring and using location metadata to personalize web search. In Proc. of SIGIR, pages 135–144. ACM, 2011. [3] P. N. Bennett, K. Svore, and S. T. Dumais. Classification-enhanced ranking. In Proc. of WWW, pages 111–120. ACM, 2010. [4] P. N. Bennett, R. W. White, W. Chu, S. T. Dumais, P. Bailey, F. Borisyuk, and X. Cui. Modeling the impact of short-and long-term behavior on search personalization. In Proc. of SIGIR, pages 185–194. ACM, 2012. [5] J. Bian, T.-Y. Liu, T. Qin, and H. Zha. Ranking with query-dependent loss for web search. In Proc. of WSDM, pages 141–150. ACM, 2010. [6] K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor. Freebase: a collabo- ratively created graph database for structuring human knowledge. In Proc. of SIG- MOD, pages 1247–1250. ACM, 2008. [7] R. L. Brennan and D. J. Prediger. Coefficient kappa: Some uses, misuses, and alter- natives. Educational and psychological measurement, 41(3):687–699, 1981. [8] C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hul- lender. Learning to rank using gradient descent. In Proc. of ICML, pages 89–96. ACM, 2005. 39[9] B. Carterette and D. Petkova. Learning a ranking from pairwise preferences. In Proc. of SIGIR, pages 629–630. ACM, 2006. [10] Y. Chen, X. Li, A. Dick, and R. Hill. Ranking consistency for image matching and object retrieval. Pattern Recognition, 47(3):1349–1360, 2014. [11] M. Farah and D. Vanderpooten. An outranking approach for rank aggregation in information retrieval. In Proc. of SIGIR, pages 591–598. ACM, 2007. [12] S. Fox, K. Karnawat, M. Mydland, S. Dumais, and T. White. Evaluating implicit measures to improve web search. ACM Transactions on Information Systems (TOIS), 23(2):147–168, 2005. [13] J. Gao, W. Yuan, X. Li, K. Deng, and J.-Y. Nie. Smoothing clickthrough data for web search ranking. In Proc. of SIGIR, pages 355–362. ACM, 2009. [14] J. Guo, G. Xu, X. Cheng, and H. Li. Named entity recognition in query. In Proc. of SIGIR, pages 267–274. ACM, 2009. [15] J. Hu, G. Wang, F. Lochovsky, J.-t. Sun, and Z. Chen. Understanding user’s query intent with wikipedia. In Proc. of WWW, pages 471–480. ACM, 2009. [16] J. Jiang, N. Yu, and C.-Y. Lin. Focus: learning to crawl web forums. In Proc. of WWW, pages 33–42. ACM, 2012. [17] T. Joachims. Optimizing search engines using clickthrough data. In Proc. of SIGKDD, pages 133–142. ACM, 2002. [18] T. Joachims, L. Granka, B. Pan, H. Hembrooke, F. Radlinski, and G. Gay. Evaluat- ing the accuracy of implicit feedback from clicks and query reformulations in web search. ACM Transactions on Information Systems (TOIS), 25(2):7, 2007. [19] M. G. Kendall. A new measure of rank correlation. Biometrika, 30(1/2):81–93, 1938. 40[20] K. Li, X. Cheng, Y. Guo, and K. Zhang. Crawling dynamic web pages in www forums. Jisuanji Gongcheng/ Computer Engineering, 33(6):80–82, 2007. [21] X. Li, Y.-Y. Wang, D. Shen, and A. Acero. Learning with click graph for query intent classification. ACM Transactions on Information Systems (TOIS), 28(3):12, 2010. [22] J. Liu, Y.-I. Song, and C.-Y. Lin. Competition-based user expertise score estimation. In Proc. of SIGIR, pages 425–434. ACM, 2011. [23] D. Metzler. Using gradient descent to optimize language modeling smoothing pa- rameters. In Proc. of SIGIR, pages 687–688. ACM, 2007. [24] T. M. Mitchell. Machine Learning. McGraw-Hill, Inc., 1997. [25] T. H. Nguyen and B. K. Szymanski. Social ranking techniques for the web. In Proc. of ASONAM, pages 49–55. ACM, 2013. [26] F. Radlinski and T. Joachims. Query chains: learning to rank from implicit feedback. In Proc. of SIGKDD, pages 239–248. ACM, 2005. [27] Y.-I. Song, J.-T. Lee, and H.-C. Rim. Word or phrase?: learning which unit to stress for information retrieval. In Proc. of ACL, pages 1048–1056. ACL, 2009. [28] H. Wang, X. He, M.-W. Chang, Y. Song, R. W. White, and W. Chu. Personalized ranking model adaptation for web search. In Proc. of SIGIR, pages 323–332. ACM, 2013. [29] K. Wang, T. Walker, and Z. Zheng. Pskip: estimating relevance ranking quality from web search clickthrough data. In Proc. of SIGKDD, pages 1355–1364. ACM, 2009. [30] W. Wu, H. Li, H. Wang, and K. Q. Zhu. Probase: A probabilistic taxonomy for text understanding. In Proc. of SIGMOD, pages 481–492. ACM, 2012. [31] X. Yin, W. Tan, X. Li, and Y.-C. Tu. Automatic extraction of clickable structured web contents for name entity queries. In Proc. of WWW, pages 991–1000, 2010. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/52999 | - |
| dc.description.abstract | 本論文提出了一個創新的概念－在網頁搜尋中的排序一致性 (ranking consistency in web search) 。相關排序 (relevance ranking) 是在創建一個有效的網頁搜尋系統時會碰到最大的問題之一。給定一些具有相似搜尋意圖 (search intents) 的查詢 (queries) ，常見的作法是將個別的查詢分別去優化排序模型 (ranking models) 。因此，在現代的搜尋引擎中，會有不一致的排序結果。但我們預期具有相似意圖的查詢應該保有排序一致性。本論文的目的在於為了提升網頁搜尋的相關排序，而學習搜尋結果的排序一致性。藉由利用知識庫 (knowledge base)與搜尋紀錄 (search logs) ，我們提出了一個同時提升相關排序與排序一致性的重新排序模型 (re-ranking model) 。據我們所知，本論文提出了第一個藉由提升排序一致性來提升相關排序效能的解法。實驗的結果也顯示出我們所提出的方法顯著地提升了相關排序以及排序一致性。兩個在群眾發包平台 (crowd-sourcing platform) Amazon Mechanical Turk 所進行的調查也顯示出使用者對於排序一致性相當敏感，也比較喜愛由我們所提出之方法所得到較為一致的排序結果。 | zh_TW |
| dc.description.abstract | In this paper, we propose a new idea called ranking consistency in web search. Relevance ranking is one of the biggest problems in creating an effective web search system. Given some queries with similar search intents, conventional approaches typically only optimize ranking models by each query separately. Hence, there are inconsistent rankings in modern search engines. It is expected that the search results of different queries with similar search intents should preserve ranking consistency. The aim of this paper is to learn consistent rankings in search results for improving the relevance ranking in web search. We then propose a re-ranking model aiming to simultaneously improve relevance ranking and ranking consistency by leveraging knowledge bases and search logs. To the best of our knowledge, our work offers the first solution to improving relevance rankings with ranking consistency. Extensive experiments have been conducted using the Freebase knowledge base and the large-scale query-log of a commercial search engine. The experimental results show that our approach significantly improves relevance ranking and ranking consistency. Two user surveys on Amazon Mechanical Turk also show that users are sensitive and prefer the consistent ranking results generated by our model. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T16:38:27Z (GMT). No. of bitstreams: 1 ntu-104-R02922026-1.pdf: 604335 bytes, checksum: 00611148f4f8b8179b88baa06d08fa77 (MD5) Previous issue date: 2015 | en |
| dc.description.tableofcontents | 口試委員會審定書 ii
誌謝 iii 摘要 v Abstract vii 1 Introduction 1 2 Related Work 5 3 Two-Stage Re-ranking Model 9 4 Consistent Ranking Model 13 4.1 Model Formalities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 4.2 Model Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.3 Pattern-Type Relevance . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.4 Type Distribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 4.5 Theoretical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 5 Ensemble-based Re-ranking 23 5.1 Model Formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 5.2 Multiple Parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 5.3 Parameter Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 5.4 Re-ranking Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 ix5.4.1 Entity Features . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 5.4.2 Query Features . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 5.4.3 URL Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 6 Experiments 29 6.1 Datasets and Experimental Settings . . . . . . . . . . . . . . . . . . . . . 29 6.2 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 6.2.1 Evaluation of Ranking Consistency . . . . . . . . . . . . . . . . 30 6.2.2 Evaluation of Pattern-Type Relevance . . . . . . . . . . . . . . . 31 6.2.3 Evaluation of Re-ranking Models . . . . . . . . . . . . . . . . . 33 6.3 User Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 7 Conclusions and Future Work 37 Bibliography 39 | |
| dc.language.iso | en | |
| dc.subject | 重新排序 | zh_TW |
| dc.subject | 排序一致性 | zh_TW |
| dc.subject | 網頁搜尋 | zh_TW |
| dc.subject | Web search | en |
| dc.subject | Ranking consistency | en |
| dc.subject | Re-ranking | en |
| dc.title | 利用知識庫與搜尋紀錄提升網頁搜尋的排序一致性 | zh_TW |
| dc.title | Improving Ranking Consistency for Web Search by Leveraging a Knowledge Base and Search Logs | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 曾新穆(Shin-Mu Tseng),蔡銘峰(Ming-Feng Tsai),張嘉惠(Chia-Hui Chang),陳信希(Hsin-Hsi Chen) | |
| dc.subject.keyword | 網頁搜尋,排序一致性,重新排序, | zh_TW |
| dc.subject.keyword | Web search,Ranking consistency,Re-ranking, | en |
| dc.relation.page | 41 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-08-12 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位： | 資訊工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-104-1.pdf 未授權公開取用 | 590.17 kB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。