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US 10,832,131 B2Granted
Semantic similarity for machine learned job posting result ranking model
AssigneeMicrosoft Technology Licensing, LLC
InventorsSaurabh Kataria, Dhruv Arya, Ganesh Venkataraman
Life of this patent
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Abstract
In an example embodiment, a machine learning algorithm is used to train a deep semantic similarity neural network to output a semantic similarity score between a candidate job search query and a candidate job search result. This semantic similarity score can then be used in a ranking phase to rank job search results in response to a first job search query.
Claims17
A system comprising:
- a non-transitory computer-readable medium having instructions stored thereon, which, when executed by a processor, cause the system to:
- obtain training data pertaining to sample job posting search queries, the training data comprising sample job posting search results and indications as to which of the sample job posting search results were selected by members performing job posting search queries corresponding to the sample job posting search results;
- for each of the sample job posting search queries, feed the training data corresponding to the sample job posting into a machine learning algorithm to train a deep semantic similarity neural network to output a semantic similarity score indicating semantic similarity between a candidate job search query and a candidate job search result;
- identify a plurality of job search results in response to a first job search query;
- for each of the plurality of job search results:
- feed the job search result and the first job search query into the deep semantic similarity neural network to obtain a semantic similarity score indicating semantic similarity between the job search result and the first job search query;
- rank the plurality of job search results based on the semantic similarity score for each of the job search results;
- transmit the ranked plurality of job search results to a client device for display;
- and
- augment the first job search query using terms that are semantically similar to terms in the first job search query, utilizing a semantic similarity score output from the deep semantic similarity neural network.
7 more elements
A computerized method, comprising:
- obtaining training data pertaining to sample job posting search queries, the training data comprising sample job posting search results and indications as to which of the sample job posting search results were selected by members performing job posting search queries corresponding to the sample job posting search results;
- for each of the sample job posting search queries, feeding the training data corresponding to the sample job posting into a machine learning algorithm to train a deep semantic similarity neural network to output a semantic similarity score indicating semantic similarity between a candidate job search query and a candidate job search result, wherein the deep semantic similarity neural network is a Siamese network;
- identifying a plurality of job search results in response to a first job search query;
- for each of the plurality of job search results:
- feeding the job search result and the first job search query into the deep semantic similarity neural network to obtain a semantic similarity score indicating semantic similarity between the job search result and the first job search query;
- ranking the plurality of job search results based on the semantic similarity score for each of the job search results;
- transmitting the ranked plurality of job search results to a client device for display.
4 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:
- obtaining training data pertaining to sample job posting search queries, the training data comprising sample job posting search results and indications as to which of the sample job posting search results were selected by members performing job posting search queries corresponding to the sample job posting search results;
- for each of the sample job posting search queries, feeding the training data corresponding to the sample job posting into a machine learning algorithm to train a deep semantic similarity neural network to output a semantic similarity score indicating semantic similarity between a candidate job search query and a candidate job search result,
- identifying a plurality of job search results in response to a first job search query;
- for each of the plurality of job search results:
- feeding the job search result and the first job search query into the deep semantic similarity neural network to obtain a semantic similarity score indicating semantic similarity between the job search result and the first job search query;
- ranking the plurality of job search results based on the semantic similarity score for each of the job search results;
- transmitting the ranked plurality of job search results to a client device for display;
- and
- augment the first job search query using terms that are semantically similar to terms in the first job search query, utilizing a semantic similarity score output from the deep semantic similarity neural network.
6 more elements
Dependencies are inferred from the claim text.