Beyond independent relevance: methods and evaluation metrics for subtopic retrieval
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
Externalities in online advertising
Proceedings of the 17th international conference on World Wide Web
Online learning from click data for sponsored search
Proceedings of the 17th international conference on World Wide Web
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A Cascade Model for Externalities in Sponsored Search
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Spatio-temporal models for estimating click-through rate
Proceedings of the 18th international conference on World wide web
A novel click model and its applications to online advertising
Proceedings of the third ACM international conference on Web search and data mining
Personalized click prediction in sponsored search
Proceedings of the third ACM international conference on Web search and data mining
Competing for users' attention: on the interplay between organic and sponsored search results
Proceedings of the 19th international conference on World wide web
Expressive auctions for externalities in online advertising
Proceedings of the 19th international conference on World wide web
Diversifying web search results
Proceedings of the 19th international conference on World wide web
The good, the bad, and the random: an eye-tracking study of ad quality in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Temporal click model for sponsored search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Multi-dimensional search result diversification
Proceedings of the fourth ACM international conference on Web search and data mining
The sum of its parts: reducing sparsity in click estimation with query segments
Information Retrieval
Bid optimizing and inventory scoring in targeted online advertising
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Do ads compete or collaborate?: designing click models with full relationship incorporated
Proceedings of the 21st ACM international conference on Information and knowledge management
Predictive model performance: offline and online evaluations
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Up or Down? Click-Through Rate Prediction from Social Intention for Search Advertising
Proceedings of International Conference on Information Integration and Web-based Applications & Services
Exploiting contextual factors for click modeling in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
Sampling dilemma: towards effective data sampling for click prediction in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
Estimating ad group performance in sponsored search
Proceedings of the 7th ACM international conference on Web search and data mining
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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.