Location-based recommendations using nearest neighbors in a locality sensitive hashing (LSH) index

Patent metadata

Field	Value
Publication number	US-11238024-B2
Application number	US-201916729802-A
Country	US
Kind code	B2
Filing date	Dec 30, 2019
Priority date	Nov 20, 2015
Publication date	Feb 1, 2022
Grant date	Feb 1, 2022

Abstract

Software for a website hosting short-text services creates an index of buckets for locality sensitive hashing (LSH). The software stores the index in an in-memory database of key-value pairs. The software creates, on a mobile device, a cache backed by the in-memory database. The software then uses a short text to create a query embedding. The software map the query embedding to corresponding buckets in the index and determines which of the corresponding buckets are nearest neighbors to the query embedding using a similarity measure. The software displays location types associated with each of the buckets that are nearest neighbors in a view in a graphical user interface(GUI) on the mobile device and receives a user selection as to one of the location types. Then the software displays the entities for the selected location type in a GUI view on the mobile device.

First claim

What is claimed is:

1. A method, comprising:
creating an index of a plurality of buckets for locality sensitive hashing (LSH), wherein a bucket of the plurality of buckets includes one or more word or phrase embeddings derived from a corpus of documents that describe entities associated with geographic locations;
mapping a query embedding to corresponding buckets in the index and determining which of the corresponding buckets are nearest neighbors to the query embedding using a similarity measure;
displaying one or more location types associated with one or more buckets that are nearest neighbors in a view in a graphical user interface (GUI) on a mobile device;
receiving a user selection of a location type of the one or more location types; and
displaying one or more entities associated with the location type in a GUI view on the mobile device.

2. The method of claim 1 , comprising ranking the one or more entities based on geographical proximity to the mobile device.

3. The method of claim 2 , comprising using the ranking to determine prominence when displaying the one or more entities.

4. The method of claim 2 , wherein the ranking is performed using a mapping app.

5. The method of claim 4 , wherein the mapping app uses at least one of a geo-location or a geo-position for the mobile device.

6. The method of claim 1 , wherein the one or more word or phrase embeddings are derived from the corpus using a continuous distribution model.

7. The method of claim 6 , wherein the continuous distribution model is at least one of a continuous bag-of-words model or a continuous skip-gram model.

8. The method of claim 1 , wherein a geographic location of the geographic locations is at least one of a geo-location or geo-position associated with the mobile device.

9. The method of claim 1 , wherein the similarity measure uses at least one of cosine similarity, city-block similarity, or Euclidian similarity.

10. One or more non-transitory computer-readable media persistently storing instructions that, when executed by a processor, perform operations comprising:
creating an index of a plurality of buckets, wherein a bucket of the plurality of buckets includes one or more word or phrase embeddings derived from a corpus of documents that describe entities associated with geographic locations;
map a query embedding to corresponding buckets in the index and determine which of the corresponding buckets are nearest neighbors to the query embedding using a similarity measure;
display one or more location types associated with one or more buckets that are nearest neighbors in a view in a graphical user interface (GUI) on a device;
receive a user selection of a location type of the one or more location types; and
display one or more entities associated with the location type in a GUI view on the device.

11. The non-transitory computer-readable media of claim 10 , the operations comprising ranking the one or more entities based on geographical proximity to the device.

12. The non-transitory computer-readable media of claim 11 , comprising using the ranking to determine prominence when displaying the one or more entities.

13. The non-transitory computer-readable media of claim 11 , wherein the ranking is performed using a mapping app.

14. The non-transitory computer-readable media of claim 13 , wherein the mapping app uses at least one of a geo-location or a geo-position for the device.

15. The non-transitory computer-readable media of claim 10 , wherein the one or more word or phrase embeddings are derived from the corpus using a continuous distribution model.

16. The non-transitory computer-readable media of claim 15 , wherein the continuous distribution model is a continuous bag-of-words model or a continuous skip-gram model.

17. The non-transitory computer-readable media of claim 10 , wherein a geographic location of the geographic locations is at least one of a geo-location or a geo-position associated with the device.

18. The non-transitory computer-readable media of claim 10 , wherein the similarity measure uses at least one of cosine similarity, city-block similarity, or Euclidian similarity.

19. A method, comprising:
creating an index of a plurality of buckets, wherein a bucket of the plurality of buckets includes one or more word or phrase embeddings derived from a corpus of documents that describe entities associated with geographic locations;
mapping a query embedding to corresponding buckets in the index and determining which of the corresponding buckets are nearest neighbors to the query embedding;
displaying one or more location types associated with one or more buckets that are nearest neighbors in a view in a graphical user interface (GUI) on a device;
receiving a user selection of a location type of the one or more location types; and
displaying one or more entities associated with the location type in a GUI view on the device.

20. The method of claim 19 , comprising ranking the one or more entities based on geographical proximity to the device.

Assignees

• Verizon Patent & Licensing Inc

Inventors

• Pappu Aasish
• Stent Amanda

Classifications

• G06F16/9014

  hash tables · CPC title

• H04W4/024

  Guidance services · CPC title

• G06F16/248

  Presentation of query results · CPC title

• G06F16/29

  Geographical information databases · CPC title

• G06F16/387

  using geographical or spatial information, e.g. location · CPC title