Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Accurately interpreting clickthrough data as implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Interpreting the data: Parallel analysis with Sawzall
Scientific Programming - Dynamic Grids and Worldwide Computing
Predicting clicks: estimating the click-through rate for new ads
Proceedings of the 16th international conference on World Wide Web
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Efficient multiple-click models in web search
Proceedings of the Second ACM International Conference on Web Search and Data Mining
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Click chain model in web search
Proceedings of the 18th international conference on World wide web
BBM: bayesian browsing model from petabyte-scale data
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Predicting bounce rates in sponsored search advertisements
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine
Proceedings of the third ACM international conference on Web search and data mining
A novel click model and its applications to online advertising
Proceedings of the third ACM international conference on Web search and data mining
Overlapping experiment infrastructure: more, better, faster experimentation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Characterizing search intent diversity into click models
Proceedings of the 20th international conference on World wide web
The sum of its parts: reducing sparsity in click estimation with query segments
Information Retrieval
User-click modeling for understanding and predicting search-behavior
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
A noise-aware click model for web search
Proceedings of the fifth ACM international conference on Web search and data mining
Proceedings of the fifth ACM international conference on Web search and data mining
Beyond ten blue links: enabling user click modeling in federated web search
Proceedings of the fifth ACM international conference on Web search and data mining
Factoring past exposure in display advertising targeting
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving searcher models using mouse cursor activity
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Usage data in web search: benefits and limitations
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Do ads compete or collaborate?: designing click models with full relationship incorporated
Proceedings of the 21st ACM international conference on Information and knowledge management
TellMyRelevance!: predicting the relevance of web search results from cursor interactions
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Exploiting contextual factors for click modeling 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
Hi-index | 0.00 |
There has been considerable work on user browsing models for search engine results, both organic and sponsored. The click-through rate (CTR) of a result is the product of the probability of examination (will the user look at the result) times the perceived relevance of the result (probability of a click given examination). Past papers have assumed that when the CTR of a result varies based on the pattern of clicks in prior positions, this variation is solely due to changes in the probability of examination. We show that, for sponsored search results, a substantial portion of the change in CTR when conditioned on prior clicks is in fact due to a change in the relevance of results for that query instance, not just due to a change in the probability of examination. We then propose three new user browsing models, which attribute CTR changes solely to changes in relevance, solely to changes in examination (with an enhanced model of user behavior), or to both changes in relevance and examination. The model that attributes all the CTR change to relevance yields substantially better predictors of CTR than models that attribute all the change to examination, and does only slightly worse than the model that attributes CTR change to both relevance and examination. For predicting relevance, the model that attributes all the CTR change to relevance again does better than the model that attributes the change to examination. Surprisingly, we also find that one model might do better than another in predicting CTR, but worse in predicting relevance. Thus it is essential to evaluate user browsing models with respect to accuracy in predicting relevance, not just CTR.