Location- and Query-Aware Modeling of Browsing and Click Behavior in Sponsored Search
Bibliographic record
Abstract
An online advertisement’s clickthrough rate provides a fundamental measure of its quality, which is widely used in ad selection strategies. Unfortunately, ads placed in contexts where they are rarely viewed—or where users are unlikely to be interested in commercial results—may receive few clicks regardless of their quality. In this article, we model the variability of a user’s browsing behavior for the purpose of click analysis and prediction in sponsored search. Our model incorporates several important contextual factors that influence ad clickthrough rates, including the user’s query and ad placement on search engine result pages. We formally model these factors with respect to the list of ads displayed on a result page, the probability that the user will initiate browsing of this list, and the persistence of the user in browsing the list. We incorporate these factors into existing click models by augmenting them with appropriate query and location biases. Using expectation maximization, we learn the parameters of these augmented models from click signals recorded in the logs of a commercial search engine. To evaluate the performance of the models and to compare them with state-of-the-art performance, we apply standard evaluation metrics, including log-likelihood and perplexity. Our evaluation results indicate that, through the incorporation of query and location biases, significant improvements can be achieved in predicting browsing and click behavior in sponsored search. In addition, we explore the extent to which these biases actually reflect varying behavioral patterns. Our observations confirm that correlations exist between the biases and user search behavior.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".