relational click prediction for sponsored search

ISCAS OpenIR

	relational click prediction for sponsored search
	Xiong Chenyan; Wang Taifeng; Ding Wenkui; Shen Yidong; Liu Tie-Yan
	2012
会议名称	5th ACM International Conference on Web Search and Data Mining, WSDM 2012
会议录名称	WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining
页码	493-502
会议日期	February 8, 2012 - February 12, 2012
会议地点	Seattle, WA, United states
收录类别	EI
ISBN	9781450307475
部门归属	(1) Graduate University Chinese Academy of Sciences Beijing China; (2) Microsoft Research Asia Beijing China; (3) Tsinghua University Beijing China; (4) Institute of Software Chinese Academy of Sciences Beijing China
摘要	This paper is concerned with the prediction of clicking an ad in sponsored search. The accurate prediction of user's click on an ad plays an important role in sponsored search, because it is widely used in both ranking and pricing of the ads. Previous work on click prediction usually takes a single ad as input, and ignores its relationship to the other ads shown in the same page. This independence assumption here, however, might not be valid in the real scenario. In this paper, we first perform an analysis on this issue by looking at the click-through rates (CTR) of the same ad, in the same position and for the same query, but surrounded by different ads. We found that in most cases the CTR varies largely, which suggests that the relationship between ads is really an important factor in predicting click probability. Furthermore, our investigation shows that the more similar the surrounding ads are to an ad, the lower the CTR of the ad is. Based on this observation, we design a continuous conditional random fields (CRF) based model for click prediction, which considers both the features of an ad and its similarity to the surrounding ads. We show that the model can be effectively learned using maximum likelihood estimation, and can also be efficiently inferred due to its closed form solution. Our experimental results on the click-through log from a commercial search engine show that the proposed model can predict clicks more accurately than previous independent models. To our best knowledge this is the first work that predicts ad clicks by considering the relationship between ads. Copyright 2012 ACM.; This paper is concerned with the prediction of clicking an ad in sponsored search. The accurate prediction of user's click on an ad plays an important role in sponsored search, because it is widely used in both ranking and pricing of the ads. Previous work on click prediction usually takes a single ad as input, and ignores its relationship to the other ads shown in the same page. This independence assumption here, however, might not be valid in the real scenario. In this paper, we first perform an analysis on this issue by looking at the click-through rates (CTR) of the same ad, in the same position and for the same query, but surrounded by different ads. We found that in most cases the CTR varies largely, which suggests that the relationship between ads is really an important factor in predicting click probability. Furthermore, our investigation shows that the more similar the surrounding ads are to an ad, the lower the CTR of the ad is. Based on this observation, we design a continuous conditional random fields (CRF) based model for click prediction, which considers both the features of an ad and its similarity to the surrounding ads. We show that the model can be effectively learned using maximum likelihood estimation, and can also be efficiently inferred due to its closed form solution. Our experimental results on the click-through log from a commercial search engine show that the proposed model can predict clicks more accurately than previous independent models. To our best knowledge this is the first work that predicts ad clicks by considering the relationship between ads. Copyright 2012 ACM.
关键词	Data Mining Information Retrieval Maximum Likelihood Estimation Search Engines Websites
主办者	Special Interest Group on Information Retrieval (ACM SIGIR); ACM Spec. Interest Group Knowl. Discov. Data Min. (SIGKDD); ACM SIGMOD; ACM SIGWEB
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/15707
专题	中国科学院软件研究所
推荐引用方式 GB/T 7714	Xiong Chenyan,Wang Taifeng,Ding Wenkui,et al. relational click prediction for sponsored search[C],2012:493-502.