Ad-centric Model Discovery for Prediciting Ads's Click-through Rate
Bibliographic record
Abstract
Click here and insert your abstract text. Search engine advertising has become one of the most important revenue models of electronic commerce. It strongly affects the probability that users click on the ads at the side of the search results page if the system shows the right ones. To maximize the outcome of search engine revenue and improve perception on those ads, it is important to understand the factors which affect the click through rate (CTR) on those ads. Tencent founded in 1998, is one of China's largest and most used Internet service portals. It provides a number of online services such as value-added Internet, mobile and telecom services and online advertising. As of September 30, 2011, Tencent had 711.7 million active Instant Messenger users. It forms the largest Internet Community in China. In this research, we use a very large dataset of Tencent click logs (soso.com) with millions records. First we describe how soso.com searching engine advertising works, our system architecture is designed with the click log dataset, and observations inside it aims at those ads with enough historical click logs. Then we show how to use ad-centric features to discover models that can find factors affecting CTR prediction performance. The proposed framework could help both optimizing the search engine system for soso.com and improving the ads designs for the advertisers.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".