SEARCH ENGINE

• US 20180107983A1
• Filed: 10/18/2016
• Published: 04/19/2018
• Est. Priority Date: 10/18/2016
• Status: Active Grant

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Mir Ghaderi, Seyed Reza, Tao, Xuejun, Tian, Ye, Courtney, Matthew, Chen, Pei-Chun, Posse, Christian
• Granted Patent: US 10,643,183 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/24578 using ranking
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06Q 10/105 Human resources
• CPC Class Codes: G06Q 10/1053 Employment or hiring

Abstract

Methods, systems, and apparatus, including computer programs encoded on storage devices, for performing a job opportunity search. In one aspect, a system includes a data processing apparatus, and a computer-readable storage device having stored thereon instructions that, when executed by the data processing apparatus, cause the data processing apparatus to perform operations. The operations include defining a vector vocabulary, defining an occupation taxonomy that includes multiple different occupations, obtaining multiple labeled training data items, wherein each labeled training data item is associated with at least (i) a job title, and (ii) an occupation, generating, for each of the respective labeled training data items, an occupation vector that includes a feature weight for each respective term in the vector vocabulary, and associating each respective occupation vector with an occupation in the occupation taxonomy based on the occupation of the labeled training data item used to generate the occupation vector.

Claims

1. A method comprising:

defining a vector vocabulary;

defining an occupation taxonomy that includes multiple different occupations;

obtaining multiple labeled training data items, wherein each labeled training data item is associated with at least (i) a job title, and (ii) an occupation;

generating, for each of the respective labeled training data items, an occupation vector that includes a feature weight for each respective term in the vector vocabulary;

associating each respective occupation vector with an occupation in the occupation taxonomy based on the occupation of the labeled training data item used to generate the occupation vector;

receiving a search query that includes a string related to a characteristic of one or more potential job opportunities;

generating a first vector based on the received query;

determining, for each respective occupation of the multiple occupations in the occupation taxonomy, a confidence score that is indicative of whether the query vector is correctly classified in the respective occupation;

selecting the particular occupation that is associated with the highest confidence score;

obtaining one or more job postings using the selected occupation; and

providing the obtained job postings in a set of search results in response to the search query.

2. The method of claim 1, wherein defining a vector vocabulary includes generating a vector vocabulary that includes terms that are present in at least one training data item stored in a text corpus and terms that are not present in at least one training data item stored in the text corpus.

3. The method of claim 1, wherein defining a vector vocabulary includes generating a vector vocabulary that includes only terms that are present in at least one training data item stored in a text corpus.

4. The method of claim 1, wherein the feature weight is based, at least in part, on a first value representing a term frequency that is determined, based at least in part, on a number of occurrences of each respective term in the job title of the respective training data item.

5. The method of claim 1, wherein the feature weight is based, at least in part, on a second value representing an inverse occupation frequency that is determined based, at least in part, on a number of occupations in the occupation taxonomy where each respective term in the job title of the respective training data item is present.

6. The method of claim 1, wherein the feature weight is based, at least in part, on a third value representing an occupation derivative that is based, at least in part, on a density of each respective term in the job title of the respective training data item across each of the respective occupations in the occupation taxonomy.

7. The method of claim 1, wherein the feature weight is based, at least in part, on both (i) a second value representing the inverse occupation frequency that is determined based, at least in part, on a number of occupations in the occupation taxonomy where each respective term in the job title of the respective training data item is present and (ii) a third value representing an occupation derivative that is based, at least in part, on a density of each respective term in the job title of the respective training data item across each of the respective occupations in the occupation taxonomy.

8. The method of claim 7, wherein the feature weight is based on a sum of (i) the second value representing the inverse occupation frequency, and (ii) one-third of the third value representing the occupation derivative.

9. A system comprising:

one or more data processing apparatus; and

one or more computer-readable storage devices having stored thereon instructions that, when executed by the one or more data processing apparatus, cause the one or more data processing apparatus to perform operations comprising;

defining a vector vocabulary;

defining an occupation taxonomy that includes multiple different occupations;

obtaining multiple labeled training data items, wherein each labeled training data item is associated with at least (i) a job title, and (ii) an occupation;

generating, for each of the respective labeled training data items, an occupation vector that includes a feature weight for each respective term in the vector vocabulary; and

associating each respective occupation vector with an occupation in the occupation taxonomy based on the occupation of the labeled training data item used to generate the occupation vector.

10. The system of claim 9, wherein the operations further comprise:

receiving a search query that includes a string related to a characteristic of one or more job opportunities;

generating a first vector based on the received query;

identifying one or more particular occupations in the occupation taxonomy that are associated with the first vector based on a calculated confidence score; and

providing the job postings in a set of search results in response to the search query.

11. The system of claim 9, wherein the feature weight is based, at least in part, on a first value representing a term frequency that is determined, based at least in part, on a number of occurrences of each respective term in the job title of the respective training data item.

12. The system of claim 9, wherein the feature weight is based, at least in part, on a second value representing an inverse occupation frequency that is determined based, at least in part, on a number of occupations in the occupation taxonomy where each respective term in the job title of the respective training data item is present.

13. The system of claim 9, wherein the feature weight is based, at least in part, on a third value representing an occupation derivative that is based, at least in part, on a density of each respective term in the job title of the respective training data item across each of the respective occupations in the occupation taxonomy.

14. The system of claim 9, wherein the feature weight is based, at least in part, on both (i) a second value representing the inverse occupation frequency that is determined based, at least in part, on a number of occupations in the occupation taxonomy where each respective term in the job title of the respective training data item is present and (ii) a third value representing an occupation derivative that is based, at least in part, on a density of each respective term in the job title of the respective training data item across each of the respective occupations in the occupation taxonomy.

15. A computer-readable storage device having stored thereon instructions, which, when executed by data processing apparatus, cause the data processing apparatus to perform operations comprising:

receiving a search query that includes a string related to a characteristic of one or more job opportunities;

generating, based on the received query, a query vector that includes a feature weight for each respective term in a predetermined vector vocabulary;

determining, for each respective occupation of the multiple occupations in the occupation taxonomy, a confidence score that is indicative of whether the query vector is correctly classified in the respective occupation;

selecting the particular occupation that is associated with the highest confidence score;

obtaining one or more job postings using the selected occupation; and

providing the obtained job postings in a set of search results in response to the search query.

16. The computer-readable medium of claim 15, wherein the predetermined vector vocabulary includes terms that are present in at least one training data item stored in a text corpus and terms that are not present in at least one training data item stored in the text corpus.

17. The computer-readable medium of claim 15, wherein the feature weight is based, at least in part, on a first value representing a term frequency that is determined, based at least in part, on a number of occurrences of each respective term in the job title of the respective training data item.

18. The computer-readable medium of claim 15, wherein the feature weight is based, at least in part, on a second value representing an inverse occupation frequency that is determined based, at least in part, on a number of occupations in the occupation taxonomy where each respective term in the job title of the respective training data item is present.

19. The computer-readable medium of claim 15, wherein the feature weight is based, at least in part, on a third value representing an occupation derivative that is based, at least in part, on a density of each respective term in the job title of the respective training data item across each of the respective occupations in the occupation taxonomy.

20. The computer-readable medium of claim 15, wherein the feature weight is based, at least in part, on both (i) a second value representing the inverse occupation frequency that is determined based, at least in part, on a number of occupations in the occupation taxonomy where each respective term in the job title of the respective training data item is present and (ii) a third value representing an occupation derivative that is based, at least in part, on a density of each respective term in the job title of the respective training data item across each of the respective occupations in the occupation taxonomy.