Generating user recommendations

Patent metadata

Field	Value
Publication number	US-2016179835-A1
Application number	US-201414573756-A
Country	US
Kind code	A1
Filing date	Dec 17, 2014
Priority date	Dec 17, 2014
Publication date	Jun 23, 2016
Grant date	—

Abstract

Briefly, embodiments of methods and/or systems of providing relevant and diverse recommendations are disclosed. For one embodiment, as an example, a system may extract structured and/or semi-structured parameters from web resources obtained from interaction logs comprising records of browsing sessions. Content from extracted parameters may be compared, using an ontology, to find relationships among web resources and query resources.

First claim

1 . A method comprising:
generating cross-domain recommendations for presentation to one or more users based at least in part on real-time web browsing, on prior web browsing, and on structured and/or semi-structured parameters extracted from one or more locations visited while browsing, the generating comprising creating semantic bridges for use in generating the cross-domain recommendations.

2 . (canceled)

3 . The method of claim 1 , wherein the creating semantic bridges comprises populating an ontology based at least in part on at least the prior web browsing and on the structured and/or semi-structured parameters extracted from the one or more locations visited while browsing.

4 . The method of claim 1 , wherein the generating comprises creating feature signal vectors for use in machine learning, wherein the feature signal vectors are based at least in part on the prior web browsing and on the structured and/or semi-structured parameters extracted from the one or more locations visited while browsing.

5 . The method of claim 4 , wherein the feature signal vectors comprise one or more classes of non-relevant and/or relevant resources.

6 . The method of claim 4 , wherein the feature signal vectors are based at least in part on similarity among semantic types.

7 . The method of claim 1 , wherein the recommendations for presentation to one or more users are based at least in part on results of machine learning generated from the real-time web browsing.

8 . The method of claim 7 , wherein the machine learning comprises SVM.

9 . An apparatus, comprising:
one or more processors to: generate cross-domain recommendations for display based at least in part on real-time web browsing of a user, on prior web browsing of the user, and on structured and/or semi-structured parameters to be extracted from one or more Web sites visited by the user, the to generate cross-domain recommendations to include creation of semantic bridges.

10 . (canceled)

11 . The apparatus of claim 9 , wherein the to create semantic bridges includes to populate an ontology based at least in part on at least the prior web browsing and on the structured and/or semi-structured parameters to be extracted.

12 . The apparatus of claim 9 , wherein the one or more processors are further to generate feature signal vectors to be used in connection with a machine learning process, wherein the feature signal vectors are to be based at least in part on the prior web browsing and on the structured and/or semi-structured parameters to be extracted.

13 . The apparatus of claim 12 , wherein the feature signal vectors are to be based at least in part on similarity among semantic types.

14 . The apparatus of claim 9 , wherein the recommendations are to be based at least in part on machine-learned results to be generated from real-time web browsing.

15 . An apparatus comprising:
means for generating cross-domain recommendations for presentation to one or more users based at least in part on real-time web browsing, on prior web browsing, and on structured and/or semi-structured parameters extracted from one or more locations visited while browsing, the means for generating cross-domain recommendations comprising means for creating semantic bridges.

16 . (canceled)

17 . The apparatus of claim 15 , wherein the means for generating cross-domain recommendations comprises means for populating an ontology based at least in part on at least the prior web browsing and on the structured and/or semi-structured parameters extracted from the one or more locations visited while browsing.

18 . The apparatus of claim 15 , wherein the means for generating cross-domain recommendations for presentation comprises means for determining feature signal vectors for use in machine learning, the feature signal vectors to be based at least in part on the prior web browsing and on the structured and/or semi-structured parameters extracted from the one or more locations visited while browsing.

19 . The apparatus of claim 15 , wherein the means for generating cross-domain recommendations for presentation further comprises means for determining similarity among semantic types of the structured and/or semi structured parameters extracted from the one or more locations visited while browsing.

20 . The apparatus of claim 15 , further comprising means for recommending for presentation to one or more users based at least in part on machine-learned results generated from the real-time web browsing.

Assignees

• Yahoo Inc

Inventors

• Mika Peter
• Blanco Roi
• Hoxha Julia

Classifications

• G06F17/30734

  Physics · mapped topic

• G06F17/30867

  Physics · mapped topic

• G06F17/3097 Primary

  Physics · mapped topic

• G06N99/005

  Physics · mapped topic

• G06N20/10

  using kernel methods, e.g. support vector machines [SVM] · CPC title