Estimating probabilities of events in sponsored search using adaptive models
US8392343B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 21, 2010 |
| Grant date | Mar 5, 2013 |
| Priority date | — |
| Expiry date | Nov 22, 2031 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06Q30/0254
- WIPO fieldIT methods for management
- WIPO sectorElectrical engineering
Abstract
A machine-learning method for estimating probability of a click event in online advertising systems by computing and comparing an aggregated predictive model (a global model) and one or more data-wise sliced predictive models (local models). The method comprises receiving training data having a plurality of features stored in a feature set and constructing a global predictive model that estimates the probability of a click event for the processed feature set. Then, partitioning the global predictive model into one or more data-wise sliced training sets for training a local model from each of the data-wise slices, and then determining whether a particular local model estimates probability of click event for the feature set better than the global model. A given feature set may be collected from historical data, and may comprise a feature vector for a plurality of query-advertisement pairs and a corresponding indicator that represents a click on the advertisement.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.