Integrated architecture searching system and method
AssigneeTsinghua University
InventorsWenwu Zhu, Xin Wang, Zhikun Wei
Life of this patent
Estimating the term…
Abstract
An integrated architecture searching system for a click-through rate prediction model is provided. The system includes a first search space configured to search for embedding vector dimensions of features and determine a matching embedding vector dimension for each pair of the features; a second search space configured to obtain a feature interaction result by searching for a feature interaction sub-network and a feature interaction combination; and a third search space configured to obtain a click-through rate prediction value by incorporating the feature interaction result into an high-order implicit feature interaction search space and performing high-order implicit feature interaction on deep networks of different layers.
Claims11
An integrated architecture searching system for a click-through rate prediction model, comprising:
- a processor;
- and
- a memory storing computer instructions which, when executed by the processor, the processor is configured to:
- search for embedding vector dimensions of features and determine a matching embedding vector dimension for each pair of the features;
- obtain a feature interaction result by searching for a feature interaction sub-network a feature interaction combination;
- and
- obtain a click-through rate prediction value by incorporating the feature interaction result into a high-order implicit feature interaction search space and performing high-order implicit feature interaction on deep networks of different layers.
An integrated architecture searching method for a click-through rate prediction model, comprising:
- searching for embedding vector dimensions of features and determining a matching embedding vector dimension for each pair of the features;
- obtaining a feature interaction result by searching for a feature interaction sub-network and a feature interaction combination;
- and
- obtaining a click-through rate prediction value by incorporating the feature interaction result into a high-order implicit feature interaction search space and performing high-order implicit feature interaction on deep networks of different layers.
A method for predicting a click-through rate, comprising:
- obtaining network click-through rate data;
- and
- obtaining a click-through rate value by inputting the network click-through rate data into a click-through rate prediction model obtained by:
- searching for embedding vector dimensions of features and determining a matching embedding vector dimension for each pair of the features;
- obtaining a feature interaction result by searching for a feature interaction sub-network and a feature interaction combination;
- and
- obtaining a click-through rate prediction value by incorporating the feature interaction result into a high-order implicit feature interaction search space and performing high-order implicit feature interaction on deep networks of different layers.
Dependencies are inferred from the claim text.