Generalized graph, rule, and spatial structure based recommendation engine

Patent metadata

Field	Value
Publication number	US-9582547-B2
Application number	US-201314083285-A
Country	US
Kind code	B2
Filing date	Nov 18, 2013
Priority date	Nov 18, 2013
Publication date	Feb 28, 2017
Grant date	Feb 28, 2017

Abstract

One embodiment of the present invention provides a recommendation system. During operation, the system receives context information associated with the user, updates a plurality of user models based on the received context information, and identifies at least one spatial data structure that stores a plurality of items. A respective item is stored within the spatial data structure based on a vector value associated with the item. The system then queries the spatial data structure to obtain a first set of recommendable items that have vector values within a predetermined range, calculates a score for each item within the set of recommendable items based on the plurality of the user models and a characterization vector associated with each item, ranks the items within the set of recommendable items based on calculated scores, and recommends one or more top-ranked items to the user.

First claim

What is claimed is:

1. A method for providing a recommendation to a user, comprising:
receiving context information associated with the user;
updating a plurality of user models based on the received context information;
identifying a spatial data structure that stores a plurality of items, wherein the spatial data structure corresponds to a vector space, and wherein a respective item is stored within the spatial data structure based on a vector value associated with the item;
determining a subspace within the vector space based on the updated user models, wherein boundaries of the subspace indicate acceptable ranges of vector values;
querying the spatial data structure to obtain a first set of recommendable items that are included in the determined subspace;
calculating a score for each item within the obtained first set of recommendable items based on the plurality of the user models and a characterization vector associated with each item, wherein calculating the score involves calculating a model-specific output for each user model and calculating a weighted sum of model-specific outputs over the plurality of user models;
ranking the items within the obtained first set of recommendable items based on calculated scores; and
recommending one or more top-ranked items to the user.

2. The method of claim 1 , wherein calculating the model-specific output involves:
identifying elements in the characterization vector that correspond to parameters of the user model; and
calculating the model-specific output as a function of the identified elements and the corresponding parameters of the user model.

3. The method of claim 1 , further comprising:
identifying an additional spatial data structure that corresponds to a different vector space;
querying the additional spatial data structure to obtain a second set of recommendable items; and
generating a combined set of recommendable items using the first and the second sets of recommendable items.

4. The method of claim 1 , wherein the plurality of items includes one or more of:
a web page;
a consumer item;
an activity;
a venue; and
a location.

5. The method of claim 1 , further comprising updating a context graph associated with the user based on the received context information.

6. The method of claim 1 , wherein the spatial data structure is segmented into cells, and wherein a respective cell stores items having vector values within boundaries of the cell.

7. A non-transitory computer-readable storage medium storing instructions that when executed by a computer cause the computer to perform a method for providing a recommendation to a user, the method comprising:
receiving context information associated with the user;
updating a plurality of user models based on the received context information;
identifying a spatial data structure that stores a plurality of items, wherein the spatial data structure corresponds to a vector space, and wherein a respective item is stored within the spatial data structure based on a vector value associated with the item;
determining a subspace within the vector space based on the updated user models, wherein boundaries of the subspace indicate acceptable ranges of vector values;
querying the spatial data structure to obtain a first set of recommendable items that are included in the determined subspace;
calculating a score for each item within the obtained first set of recommendable items based on the plurality of the user models and a characterization vector associated with each item, wherein calculating the score involves calculating a model-specific output for each user model and calculating a weighted sum of model-specific outputs over the plurality of user models;
ranking the items within the obtained first set of recommendable items based on calculated scores; and
recommending one or more top-ranked items to the user.

8. The computer-readable storage medium of claim 7 , wherein calculating the model-specific output involves:
identifying elements in the characterization vector that correspond to parameters of the user model; and
calculating the model-specific output as a function of the identified elements and the corresponding parameters of the user model.

9. The computer-readable storage medium of claim 7 , wherein the method further comprises:
identifying an additional spatial data structure that corresponds to a different vector space;
querying the additional spatial data structure to obtain a second set of recommendable items; and
generating a combined set of recommendable items using the first and the second sets of recommendable items.

10. The computer-readable storage medium of claim 7 , wherein the plurality of items includes one or more of:
a web page;
a consumer item;
an activity;
a venue; and
a location.

11. The computer-readable storage medium of claim 7 , wherein the method further comprises updating a context graph associated with the user based on the received context information.

12. The computer-readable storage medium of claim 7 , wherein the spatial data structure is segmented into cells, and wherein a respective cell stores items having vector values within boundaries of the cell.

13. A recommendation computer system for providing a recommendation to a user, comprising:
a processor;
a memory;
an activity-detection module configured to detect user activities and/or interests based on context information associated with the user;
a model-updating mechanism configured to update a plurality of user models based on an output of the activity-detection module;
a spatial data structure configured to store a plurality of items, wherein the spatial data structure corresponds to a vector space, and wherein a respective item is stored within the spatial data structure based on a vector value associated with the item;
a subspace-determination module configured to determine a subspace within the vector space based on the updated user models, wherein boundaries of the subspace indicate acceptable ranges of vector values;
a querying mechanism configured to query the spatial data structure to obtain a first set of recommendable items that are included in the determined subspace; and
a mixed-model recommender configured to:
calculate a score for each item within the obtained first set of recommendable items based on the plurality of the user models and a characterization vector associated with each item, wherein calculating the score involves calculating a model-specific output for each user model and calculating a weighted sum of model-specific outputs over the plurality of user models;
rank the items within the obtained first set of recommendable items based on calculated scores; and
recommend one or more top-ranked items to the user.

14. The recommendation system of claim 13 , wherein while calculating the model-specific output, the mixed-model recommender is further configured to:
identify elements in the characterization vector that correspond to parameters of the user model; and
calculate the model-specific output as a function of the identified elements and the corresponding parameters of the user model.

15. The recommendation system of claim 13 , further comprising:
an additional spatial data structure that corresponds to a different vector space;
wherein the querying mechanism is further configured to:
query the additional spatial data structure to obtain a second set of recommendable items; and
generate a combined set of recommendable items using the first and the second sets of recommendable items.

16. The recommendation sy

Assignees

• Palo Alto Res Ct Inc

Inventors

• Roberts Michael
• Ahern Shane P

Classifications

• G06F17/30333

  Physics · mapped topic

• G06F17/30702

  Physics · mapped topic

• G06F17/3069

  Physics · mapped topic

• G06Q30/06

  Buying, selling or leasing transactions · CPC title

• G06F17/30528

  Physics · mapped topic