relational click prediction for sponsored search
文献类型：会议论文
| 作者 | Xiong Chenyan ; Wang Taifeng ; Ding Wenkui ; Shen Yidong ; Liu Tie-Yan |
| 出版日期 | 2012 |
| 会议名称 | 5th ACM International Conference on Web Search and Data Mining, WSDM 2012 |
| 会议日期 | February 8, 2012 - February 12, 2012 |
| 会议地点 | Seattle, WA, United states |
| 关键词 | Data mining Information retrieval Maximum likelihood estimation Search engines Websites |
| 页码 | 493-502 |
| 中文摘要 | 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. |
| 英文摘要 | 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. |
| 收录类别 | EI |
| 会议主办者 | Special Interest Group on Information Retrieval (ACM SIGIR); ACM Spec. Interest Group Knowl. Discov. Data Min. (SIGKDD); ACM SIGMOD; ACM SIGWEB |
| 会议录 | WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining
 |
| 语种 | 英语 |
| ISBN号 | 9781450307475 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/15707]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 推荐引用方式 GB/T 7714 | Xiong Chenyan,Wang Taifeng,Ding Wenkui,et al. relational click prediction for sponsored search[C]. 见:5th ACM International Conference on Web Search and Data Mining, WSDM 2012. Seattle, WA, United states. February 8, 2012 - February 12, 2012. |
入库方式： OAI收割
来源：软件研究所
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