US 2020/0401644 A1Published application

Two-stage training with non-randomized and randomized data

AssigneeMicrosoft Technology Licensing, LLC

InventorsDaniel Sairom Krishnan Hewlett, Dan Liu, Qi Guo, Wenxiang Chen, Xiaoyi Zhang, Lester Gilbert Cottle, Xuebin Yan, Yu Gong, Haitong Tian, Siyao Sun, Pei-Lun Liao

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Abstract

In an example embodiment, position bias and other types of bias may be compensated for by using two-phase training of a machine-learned model. In a first phase, the machine-learned model is trained using non-randomized training data. Since certain types of machine-learned models, such as those involving deep learning (e.g., neural networks) require a lot of training data, this allows the bulk of the training to be devoted to training using non-randomized training data. However, since this non-randomized training data may be biased, a second training phase is then used to revise the machine-learned model based on randomized training data to remove the bias from the machine-learned model. Since this randomized training data may be less plentiful, this allows the deep learning machine-learned model to be trained to operate in an unbiased manner without the need to generate additional randomized training data.

Claims20

Dependent claims

A system for returning search results in an online computer system, the system comprising:

a computer readable medium having instructions stored there on, which, when executed by a processor, cause the system to:
retrieve training data, the training data comprising a plurality of search results, a plurality of sample labels, and activity and usage information pertaining to actions taken by users on the search results on an online service, the training data comprising randomized and non-randomized training data, the randomized training data derived from historical presentation of a first subset of the plurality of search results using a randomized ranking and the non-randomized training data derived from historical presentation of a second subset of the plurality of search results using a non-randomized ranking;
for each sample search result in a plurality of sample search results in the non-randomized training data:
parse the sample search result and the activity and usage information pertaining to the sample search result in the non-randomized training data to extract a first set of one or more features;
feed a sample member label corresponding to the sample search result and the extracted first set of one or more features into a first machine learning algorithm to train a machine-learned model to output a score indicative of a probability that an action will be taken, in the online service, pertaining to the sample search result;
for each sample search result in a plurality of sample search results in the randomized training data:
parse the sample search result and the activity and usage information pertaining to the sample search result in the randomized training data to extract a second set of one or more features;
and
feed a sample member label corresponding to the sample search result in the randomized training data and the extracted second set of one or more features into the first machine learning algorithm to revise the machine-learned model.

6 more elements

A computer-implemented method for returning search results in an online computer system, the method comprising:

retrieving training data, the training data comprising a plurality of search results, a plurality of sample labels, and activity and usage information pertaining to actions taken by users on the search results on an online service, the training data comprising randomized and non-randomized training data, the randomized training data derived from historical presentation of a first subset of the plurality of search results using a randomized ranking and the non-randomized training data derived from historical presentation of a second subset of the plurality of search results using a non-randomized ranking;
for each sample search result in a plurality of sample search results in the non-randomized training data:
parsing the sample search result and the activity and usage information pertaining to the sample search result in the non-randomized training data to extract a first set of one or more features;
feeding a sample member label corresponding to the sample search result and the extracted first set of one or more features into a first machine learning algorithm to train a machine-learned model to output a score indicative of a probability that an action will be taken, in the online service, pertaining to the sample search result;
for each sample search result in a plurality of sample search results in the randomized training data:
parsing the sample search result and the activity and usage information pertaining to the sample search result in the randomized training data to extract a second set of one or more features;
and
feeding a sample member label corresponding to the sample search result in the randomized training data and the extracted second set of one or more features into the first machine learning algorithm to revise the machine-learned model.

5 more elements

A non-transitory machine-readable storage medium comprising instructions, which when implemented by one or more machines, cause the one or more machines to perform operations comprising:

retrieving training data, the training data comprising a plurality of search results, a plurality of sample labels, and activity and usage information pertaining to actions taken by users on the search results on an online service, the training data comprising randomized and non-randomized training data, the randomized training data derived from historical presentation of a first subset of the plurality of search results using a randomized ranking and the non-randomized training data derived from historical presentation of a second subset of the plurality of search results using a non-randomized ranking;
for each sample search result in a plurality of sample search results in the non-randomized training data:
parsing the sample search result and the activity and usage information pertaining to the sample search result in the non-randomized training data to extract a first set of one or more features;
feeding a sample member label corresponding to the sample search result and the extracted first set of one or more features into a first machine learning algorithm to train a machine-learned model to output a score indicative of a probability that an action will be taken, in the online service, pertaining to the sample search result;
for each sample search result in a plurality of sample search results in the randomized training data:
parsing the sample search result and the activity and usage information pertaining to the sample search result in the randomized training data to extract a second set of one or more features;
and
feeding a sample member label corresponding to the sample search result in the randomized training data and the extracted second set of one or more features into the first machine learning algorithm to revise the machine-learned model.

5 more elements

Dependencies are inferred from the claim text.

Facts

Application: 16/449,149Filed 21 Jun 2019
Published: 24 Dec 2020
Term ends: Not estimatedThe source records don’t support an estimate.

Classification13 codes

G06F 16/9538
Querying, e.g. by the use of web search engines: presentation of query results
G06N 3/084
Learning methods: backpropagation, e.g. using gradient descent
G06F 16/2237
Indexing structures: vectors, bitmaps or matrices
G06F 40/205
Natural language analysis: parsing

Family

US 11,204,973 B2Related patent
US 2020/0401644 A1This patent

From the US filing history. Foreign family members aren’t listed.

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