Context-Aware Recommendation Module Using Multiple Models

• US 20110131077A1
• Filed: 12/01/2009
• Published: 06/02/2011
• Est. Priority Date: 12/01/2009
• Status: Abandoned Application

• Current Assignee: Microsoft Technology Licensing LLC (Microsoft Corporation)
• Original Assignee: Microsoft Corporation
• Inventors: Tan, Ming
• Application Number:
  US12/628,237
• Publication Number:
  US 20110131077A1
• Time in Patent Office: Days
• US Class Current: 705/7.29
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 30/0224 based on user history
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products

Abstract

A hybrid recommendation module leverages multiple sources of information to generate robust and personalized recommendations. The recommendation module provides an initial set of items by considering context information. The context information pertains to at least one environmental factor that has a bearing on the relevancy of recommended items (such as location, time, etc.). The recommendation module then produces a ranked set of items, selected from among the initial set of items, based on a user'"'"'s past preference, using two or more recommendation models. The recommendation module can be applied in various environments, such as an environment which accommodates the use of mobile devices. The recommendation module can also be applied to various types of recommended items, such as establishment (e.g., store) items, product items, coupon items, etc. As to the last-mentioned type of items, the recommendation module can recommend coupon items using at least two modes of operation.

Claims

1. A method, implemented by a computer device, for recommending personalized items for a user, comprising:

receiving context information that originates from at least one source of context information;

receiving model information that originates from at least one source of model information;

providing an initial set of items based on the context information, the initial set of items having a plurality of candidate items;

providing a ranked set of items selected from among the initial set of items, based on the model information, together with user preference information, said providing using at least two recommendation models;

providing an output set of items selected on the basis of the ranked set of items, based on at least one portfolio management consideration; and

sending the output set of items to a recipient module for consumption by the user.

2. The method of claim 1, wherein at least one item in the output set of items corresponds to an establishment item which identifies an establishment.

3. The method of claim 1, wherein at least one item in the output set of items corresponds to a coupon item which identifies a coupon.

4. The method of claim 1, wherein the recipient module is a mobile device which receives the output set of items via wireless transmission.

5. The method of claim 1, wherein the context information includes at least one of:

temporal information which identifies at least one of a date or a time;

location information which identifies a present location of the user;

or

mood information which identifies a current state of mind of the user.

6. The method of claim 1, wherein the model information originates from at least two sources of model information, said at least two sources of model information selected from among:

a content-based source of information which provides feature-related information regarding features of a plurality of items;

a collaboration-based source of information which provides behavioral information regarding user preferences associated with the plurality of items; and

a friends-based source of information which provides friends-related information regarding relationships among a plurality of users.

7. The method of claim 6, further comprising formulating and supplying the model information, said formulating comprising:

formulating item-to-item content-based information based on the feature-related information obtained from the content-based source of information;

formulating item-to-item collaboration information based on the behavioral information obtained from the collaboration-based source of information; and

formulating friend-to-friend information based on the friends-related information obtained from the friends-based source of information.

8. The method of claim 6, further comprising formulating and supplying hybrid model information that:

combines feature-related information with behavioral information; and

/or combines friends-related information with user-related information.

9. The method of claim 1, wherein said providing of the ranked set of items comprises:

providing for each candidate item in the initial set of items, at least two model scores based on said at least two respective recommendation models; and

generating, for each candidate item, a combined score based on said at least two model scores.

10. The method of claim 9, wherein said generating of the combined score comprises forming a weighted combination of said at least two model scores.

11. The method of claim 10, further comprising receiving weighting factors that govern the weighted combination.

12. The method of claim 1, further comprising:

detecting that a noise condition prevails which affects quality of information received from at least one source of model information; and

adjusting, based on said detecting, at least one configuration setting that counteracts said noise condition.

13. The method of claim 12, wherein said adjusting results in favoring a model score based on a content-based source of model information if the noise condition pertains to a start-up condition.

14. The method of claim 1, wherein said at least one portfolio management consideration relates to a degree of variation of items in the output set of items.

15. The method of claim 1, further comprising selecting a set of coupon items associated with the ranked set of items, wherein the output set of items that are sent to the user pertains to coupon items.

16. A computer-implemented recommendation module for recommending items for a user, comprising:

a selector module configured to receive context information from at least one source of context information, and, in response, to provide an initial set of items based on the context information;

a ranker module configured to receive model information from at least one source of model information, and, in response, to provide a ranked set of items on the basis of the initial set of items; and

a coupon identification module configured to select a set of coupon items associated with the ranked set of items.

17. The computer-implemented recommendation module of claim 16, wherein the ranker module is configured to provide the ranked set of items using plural recommendation models which generate respective model scores.

18. A computer readable medium for storing computer readable instructions, the computer readable instructions providing a recommendation module when executed by one or more processing devices, the computer readable instructions comprising:

logic configured to receive context information from at least one source of context information;

logic configured to receive model information from at least one source of model information, at least part of the model information pertaining to coupon information;

logic configured to provide an initial set of coupon items based on the context information, the initial set of coupon items having a plurality of candidate coupon items;

logic configured to provide a ranked set of coupon items selected from among the initial set of coupon items, based on the model information that pertains to the coupon information; and

logic configured to provide an output set of coupon items selected on the basis of the ranked set of coupon items.

19. The computer readable medium of claim 18, wherein said logic configured to provide a ranked set of coupon items comprises:

logic configured to provide at least two model scores associated with each candidate coupon item in the initial set of coupon items, based on at least two respective recommendation models; and

logic configured to generate a combined similarity score for each candidate item based on said at least two model scores.

20. The computer readable medium of claim 19, wherein said at least two model scores are based on coupon-related aspects of each candidate coupon item.