IoT enhanced search results

1. A system, comprising:

an Internet of Things (IoT) entity component configured to obtain IoT device data relating to users and to supply the IoT device data to a knowledge index to identify entities in the IoT device data, the IoT entity component further configured to generate structured loT device data that associates the entities in the IoT device data with an individual user;

storage that includes an IoT entity feeds index that includes the entities from the structured loT device data; and,

an IoT enhanced ranker configured to:

obtain search results for a search query entered by the individual user, the search results including documents that include one or more query terms from the search query,

obtain the entities from the structured loT device data associated with the individual user,

rank the search results utilizing a first level ranker that comprises a machine learning model that ranks the documents based at least in part upon occurrences of the query terms in the search results, where more occurrences of the query terms in an individual document causes the document to be ranked higher than another document with fewer occurrences of the query terms,

re-rank the search results from the first level ranker utilizing a second level ranker that utilizes the entities from the structured IoT device data associated with the individual user to perform the re-ranking based at least in part upon occurrences of the entities from the structured IoT device data associated with the individual user in the ranked documents where more occurrences of the entities from the structured IoT device data cause the another document to be re-ranked higher than the individual document with fewer entities from the structured IoT device data, and,

cause at least a subset of the search results ranked by both the first level ranker and the second level ranker to be presented for the individual user.

2. The system of claim 1 , wherein the system includes the knowledge index or wherein the IoT entity component is configured to communicate with the knowledge index associated with a third party.

3. The system of claim 1 , wherein the IoT entity feeds index can include multiple fields per individual entity.

4. The system of claim 3 , wherein individual fields include user identification (ID), entity identification (ID), and/or popularity index.

5. The system of claim 4 , wherein the popularity index is a score of relevance that is a measure of trending interest in the individual entity.

6. The system of claim 1 , wherein the IoT entity feeds index is stored in indexable data format.

7. The system of claim 1 , wherein the search results comprise a list of uniform resource locator (URL) addresses.

8. The system of claim 7 , wherein the URL addresses are associated with documents and/or images.

9. The system of claim 7 , wherein the IoT enhanced ranker comprises a third level ranker.

10. The system of claim 9 , wherein the third level ranker is configured to merge multiple paths from the second level ranker.

11. The system of claim 10 , wherein the multiple paths are established by the first level ranker and relate to individual search terms of the search query.

12. A method, comprising:

obtaining Internet of Things (IoT) device data relating to users;

identifying entities in the IoT device data using a knowledge index;

generating structured IoT device data that associates the entities in the IoT device data with an individual user;

storing an IoT entity feeds index that includes the entities from the structured IoT device data;

obtaining search results for a search query entered by the individual user, the search results including documents that include one or more query terms from the search query;

obtaining the entities from the structured IoT device data associated with the individual user;

ranking the search results utilizing a first level ranker that comprises a machine learning model that ranks the documents based at least in part upon occurrences of the query terms in the search results, where more occurrences of the query terms in an individual document causes the document to be ranked higher than another document with fewer occurrences of the query terms;

without receiving user input relative to the IoT device data, re-ranking the search results from the first level ranker utilizing a second level ranker that utilizes the entities from the structured IoT device data associated with the individual user to perform the re-ranking based at least in part upon occurrences of the entities from the structured IoT device data associated with the individual user in the ranked documents where more occurrences of the entities from the structured IoT device data cause the another document to be re-ranked higher than the individual document with fewer entities from the structured IoT device data; and

causing at least a subset of the search results ranked by both the first level ranker and the second level ranker to be presented for the individual user.

13. The method of claim 12 , wherein the IoT entity feeds index includes multiple fields per individual entity.

14. The method of claim 13 , wherein individual fields include user identification, entity identification, and/or popularity index.

15. The method of claim 14 , wherein the popularity index is a score of relevance that is a measure of trending interest in the individual entity.

16. The method of claim 12 , wherein the IoT entity feeds index is stored in an indexable data format.

17. The method of claim 12 , wherein the search results comprise a list of uniform resource locator (URL) addresses.

18. The method of claim 17 , wherein the URL addresses are associated with documents and/or images.

19. The method of claim 17 , further comprising:

merging multiple paths from the second level ranker using a third level ranker.

20. The method of claim 19 , wherein the multiple paths are established by the first level ranker and relate to individual search terms of the search query.