Systems and methods for providing recommendations based on collaborative and/or content-based nodal interrelationships

• US 8,170,971 B1
• Filed: 09/28/2011
• Issued: 05/01/2012
• Est. Priority Date: 09/28/2011
• Status: Active Grant

• Current Assignee: Nara Logics, Inc.
• Original Assignee: AVA, Inc.
• Inventors: Wilson, Nathan R., Hueske, Emily A., Copeman, Thomas C.
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): Bharadwaj, Kalpana
• Application Number:
  US13/247,289
• Time in Patent Office: 216 Days
• Field of Search: 706/46
• US Class Current: 706/46
• CPC Class Codes: G06N 3/02 Neural networks
• CPC Class Codes: G06N 5/02 Knowledge representation; Symbolic representation
• CPC Class Codes: G06N 5/022 Knowledge engineering; Knowledge acquisition
• CPC Class Codes: G06N 5/04 Inference or reasoning models
• CPC Class Codes: G06Q 20/203 Inventory monitoring
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0282 Rating or review of business operators or products
• CPC Class Codes: H04L 67/535 Tracking the activity of the user network monitoring arrangements H04L43/00; recording of computer activity G06F11/34

Abstract

In selected embodiments a recommendation generator builds a network of interrelationships between venues, reviewers and users based on their attributes and reviewer and user reviews of the venues. Each interrelationship or link may be positive or negative and may accumulate with other links (or anti-links) to provide nodal links the strength of which are based on commonality of attributes among the linked nodes and/or common preferences that one node, such as a reviewer, expresses for other nodes, such as venues. The links may be first order (based on a direct relationship between, for instance, a reviewer and a venue) or higher order (based on, for instance, the fact that two venue are both liked by a given reviewer). The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by aggregating the link matrices and determining the venues which are most strongly coupled to the user.

Claims

1. A computer-implemented method comprising:

receiving attribute data for a plurality of users, the data relating to a plurality of attributes of a user and to at least a first venue for which the user has an affinity;

receiving venue data for a plurality of venues, the venue data relating to a plurality of attributes of the venues;

receiving review data for the plurality of venues, the review data reflecting the affinity of a plurality of reviewers for the plurality of venues;

accessing a data network comprising nodes corresponding at least to the plurality of venues and the plurality of reviewers and further comprising links between said nodes, each link reflecting a strength of an interrelationship between at least two nodes, wherein at least a plurality of the links strengths are a function of at least the review data and the venue data and wherein at least a plurality of the link strengths are further a function of both content-based and collaborative interrelationships and are based in part on higher order interrelationships between venues;

determining, based on the link strengths and at least one venue parameter, a plurality of recommended venues having the strongest links to a user;

generating recommendation data comprising at least one recommend venue; and

serving to a client device the recommendation data for display on a screen of the client device.

2. The method of claim 1, wherein at least a plurality of the link strengths are further a function of content-collaborative interrelationships.

3. The method of claim 1, wherein at least a plurality of the links are directionally asymmetric.

4. The method of claim 1, wherein the data network is configured to be dynamically updated by localized arithmetic operations on affected links.

5. The method of claim 1, wherein the data network comprises a first data structure for collaborative links and a second data structure, discrete from the first structure, for content-based links.

6. The method of claim 1, wherein the review data is gathered from third party review web sites.

7. The method of claim 1, further comprising retrieving and parsing cached search engine pages including reviews of venues.

8. The method of claim 1, where the link strengths are based in part on connection growth from collaborative interrelationships.

9. The method of claim 1, where the link strengths are based in part on connection creep from collaborative interrelationships.

10. The method of claim 1, where the link strengths are based in part on second order interrelationships between venues.

11. The method of claim 1, further comprising serving a user interface comprising an overview section vertically disposed above a venue comparison section which is vertically disposed above a reservations section.

12. A computer-implemented method for providing venue recommendations on a client device, comprising:

transmitting, from the client device to a server device, attribute data for a user, the data relating to a plurality of attributes of a user and to at least a first venue for which the user has an affinity;

transmitting, from the client device to a server device, a recommendation request including at least one venue attribute;

receiving, from a server device, data identifying a plurality of recommend venues, each recommend venue being selected based on the strength of a nodal interrelationship between the venue and the user within a data network comprising nodes corresponding at least to a plurality of venues and a plurality of reviewers and further comprising links between said nodes, each link reflecting a strength of an interrelationship between at least two nodes, wherein at least a plurality of the link strengths are a function of venue data relating to a plurality of attributes of the venues and review data reflecting the affinity of a plurality of reviewers for the plurality of venues and wherein at least a plurality of the links strengths are further a function of both content-based and collaborative interrelationships and are based in part on higher order interrelationships between venues; and

displaying, on a screen of the client device, data identifying the plurality of recommended venues.

13. The method of claim 12, wherein at least a plurality of the link strengths are further a function of content-collaborative interrelationships.

14. The method of claim 12, wherein at least a plurality of the links are directionally asymmetric.

15. The method of claim 12, wherein the data network is configured to be dynamically updated by localized arithmetic operations on affected links.

16. The method of claim 12, wherein the data network comprises a first data structure for collaborative links and a second data structure, discrete from the first structure, for content-based links.

17. The method of claim 12, wherein the review data is gathered from third party review web sites.

18. The method of claim 12, further comprising retrieving and parsing cached search engine pages including reviews of venues.

19. The method of claim 12, where the link strengths are based in part on connection growth from collaborative interrelationships.

20. The method of claim 12, where the link strengths are based in part on connection creep from collaborative interrelationships.

21. The method of claim 12, where the link strengths are based in part on second order interrelationships between venues.

22. The method of claim 12, wherein each affinity is optionally negative.

23. The method of claim 12, further comprising displaying, on the client device, a user interface comprising an overview section vertically disposed above a venue comparison section which is vertically disposed above a reservations section.