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Directly Optimize Diversity Evaluation Measures: A New Approach to Search Result Diversification

文献类型：期刊论文

作者Xu, Jun1,2; Xia, Long1,2; Lan, Yanyan1,2; Guo, Jiafeng1,2; Cheng, Xueqi1,2
刊名ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
出版日期2017-04-01
卷号8期号:3页码:26
关键词Search result diversification relational learning to rank diversity evaluation measure
ISSN号2157-6904
DOI10.1145/2983921
英文摘要The queries issued to search engines are often ambiguous or multifaceted, which requires search engines to return diverse results that can fulfill as many different information needs as possible; this is called search result diversification. Recently, the relational learning to rank model, which designs a learnable ranking function following the criterion of maximal marginal relevance, has shown effectiveness in search result diversification [Zhu et al. 2014]. The goodness of a diverse ranking model is usually evaluated with diversity evaluation measures such as alpha-NDCG [Clarke et al. 2008], ERR-IA [Chapelle et al. 2009], and D#-NDCG [Sakai and Song 2011]. Ideally the learning algorithm would train a ranking model that could directly optimize the diversity evaluation measures with respect to the training data. Existing relational learning to rank algorithms, however, only train the ranking models by optimizing loss functions that loosely relate to the evaluation measures. To deal with the problem, we propose a general framework for learning relational ranking models via directly optimizing any diversity evaluation measure. In learning, the loss function upper-bounding the basic loss function defined on a diverse ranking measure is minimized. We can derive new diverse ranking algorithms under the framework, and several diverse ranking algorithms are created based on different upper bounds over the basic loss function. We conducted comparisons between the proposed algorithms with conventional diverse ranking methods using the TREC benchmark datasets. Experimental results show that the algorithms derived under the diverse learning to rank framework always significantly outperform the state-of-the-art baselines.
资助项目973 Program of China[2014CB340401] ; 973 Program of China[2013CB329606] ; 863 Program of China[2014AA015204] ; 863 Program of China[2015AA020104] ; National Natural Science Foundation of China (NSFC)[61232010] ; National Natural Science Foundation of China (NSFC)[61425016] ; National Natural Science Foundation of China (NSFC)[61472401] ; National Natural Science Foundation of China (NSFC)[61203298] ; Youth Innovation Promotion Association CAS[20144310] ; Youth Innovation Promotion Association CAS[2016102]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000400160800008
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/6956]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Xu, Jun
作者单位1.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, 6 Kexueyuan South Rd, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Xu, Jun,Xia, Long,Lan, Yanyan,et al. Directly Optimize Diversity Evaluation Measures: A New Approach to Search Result Diversification[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2017,8(3):26.
APA Xu, Jun,Xia, Long,Lan, Yanyan,Guo, Jiafeng,&Cheng, Xueqi.(2017).Directly Optimize Diversity Evaluation Measures: A New Approach to Search Result Diversification.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,8(3),26.
MLA Xu, Jun,et al."Directly Optimize Diversity Evaluation Measures: A New Approach to Search Result Diversification".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 8.3(2017):26.

入库方式： OAI收割

来源：计算技术研究所

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