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

doi:10.1145/2983921

	Directly Optimize Diversity Evaluation Measures: A New Approach to Search Result Diversification
	Xu, Jun 1,2; Xia, Long 1,2; Lan, Yanyan 1,2; Guo, Jiafeng 1,2; Cheng, Xueqi 1,2
	2017-04-01
发表期刊	ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
ISSN	2157-6904
卷号	8 期号:3 页码:26
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.
关键词	Search result diversification relational learning to rank diversity evaluation measure
DOI	10.1145/2983921
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Information Systems
WOS记录号	WOS:000400160800008
出版者	ASSOC COMPUTING MACHINERY
引用统计	被引频次：16[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/6957
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.