Apparatus and method for providing harmonized recommendations based on an integrated user profile

• US 9,009,088 B2
• Filed: 04/17/2014
• Issued: 04/14/2015
• Est. Priority Date: 09/28/2011
• Status: Active Grant

• Current Assignee: Nara Logics, Inc.
• Original Assignee: Nara Logics, Inc.
• Inventors: Wilson, Nathan R., Li, Luyao, Hueske, Emily A., Kenyon, Eleanor C., Copeman, Thomas C.
• Primary Examiner(s): Chaki, Kakali
• Assistant Examiner(s): COUGHLAN, PETER D
• Application Number:
  US14/255,712
• Publication Number:
  US 20140280226A1
• Time in Patent Office: 362 Days
• US Class Current: 706/15
• CPC Class Codes: G06F 16/9535 Search customisation based on user profiles and personalisation
• CPC Class Codes: G06N 3/063 using electronic means
• CPC Class Codes: G06Q 30/0269 based on user profile or attribute
• CPC Class Codes: G06Q 30/0631 Item recommendations
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

In certain implementations, a system may receive attribute data corresponding to attributes of a plurality of users and to one or more venues for which the plurality of users has an affinity. A user personality matrix may be calculated for one or more of the plurality of users based on interrelational nodal link strengths between the one or more users and the venues. The user personality matrices may be merged to calculate a combined personality matrix representing a unified taste profile for the one or more users. A candidate list of venues having the highest link strength with the combined personality matrix may be determined. One or more recommended venues from the candidate list of venues that have the strongest links to the combined personality matrix may be determined, and recommendation data corresponding to the recommended venues may be output.

Claims

1. A method comprising:

receiving, at least one server, attribute data for a plurality of users, the attribute data relating to a plurality of attributes of the plurality of users and to one or more venues for which the plurality of users has an affinity;

receiving, at the at least one server, venue data for a plurality of venues, the plurality of venues including the one or more venues for which the plurality of users have the affinity, the venue data relating to a plurality of attributes of the plurality of venues;

accessing, via the at least one server, a data network comprising nodes corresponding at least to the plurality of venues and the plurality of users, and further comprising links between said nodes, each link reflecting a strength of an interrelationship between at least two nodes;

calculating, at the server and from the data network, user personality profiles for at least a first user and a second user of the plurality of users, wherein the user personality profiles each include data relating to the link strengths between the first user and the one or more venues for which the first user has the affinity and the second user and the one or more venues for which the second user has the affinity;

calculating, at the server and from the user personality profiles, a combined personality profile by calculating one or more composite link strengths representing a combination of the data relating to the link strengths from the user personality profiles;

determining, at the at least one server and based on the one or more composite link strengths and at least one venue attribute, one or more recommended venues that have the strongest links to the combined personality profile;

generating, at the at least one server, recommendation data comprising at least one recommended venue of the one or more recommended venues; and

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

2. The method according to claim 1, wherein the plurality of venues include at least one of restaurants, hotels, and theaters.

3. The method according to claim 1, wherein the venue data includes categorical property descriptors and corresponding property attributes for the plurality of venues.

4. The method according to claim 1, wherein:

the user personality profiles further include data relating to the link strength between the at least first and second users, and

the calculation of the combined personality profile includes calculating one or more composite link strengths representing a combination of the data relating to the link strength between the at least first and second users.

5. The method according to claim 4, wherein the recommendation data includes at least one recommended user of the plurality of users.

6. The method according to claim 1, wherein the calculation of the combined personality profile is a function of a mathematical blending of the data relating to the link strengths from the user personality profiles.

7. The method according to claim 1, further comprising:

determining, at the at least one server, a candidate list of venues based on the combined personality profile, wherein

the candidate list of venues includes at least one of the one or more venues for which the at least first and second users have the affinity and additional venues sharing a nodal interrelationship with the one or more venues for which the at least first and second users have the affinity.

8. The method according to claim 1, wherein

at least a plurality of the link strengths are a function of at least the venue data.

9. The method according to claim 8, further comprising:

calculating, at the at least one server and as part of the user personality profiles, weights corresponding to the at least first and second users'"'"' affinity for each venue attribute of the one or more venues for which the at least first and second users have the affinity; and

determining, at the at least one server and based at least on the weights, an overall score for each venue attribute in the at least first and second users'"'"' user personality profiles.

10. The method according to claim 9, wherein the weights are calculated based on the attribute data for the at least first and second users.

11. The method according to claim 9, wherein the weights are calculated based on the link strength between the at least first and second users.

12. The method according to claim 9, wherein the weights are calculated based on the attributes of the one or more venues for which the at least first and second users have the affinity.

13. The method according to claim 9, wherein the overall scores in the at least first and second users'"'"' user personality profiles are combined to generate the combined personality profile.

14. The method according to claim 8, further comprising:

receiving, at the at least one server, filter conditions corresponding to at least one of the one or more venues for which the at least first and second users have the affinity and the one or more venue attributes of the one or more venues for which the at least first and second users have the affinity.

15. The method according to claim 14, further comprising:

adjusting, at the at least one server and based on the filter conditions, at least one of the user personality profiles, the recommendation data, and the combined personality profile.

16. The method according to claim 1, wherein

calculating the one or more composite link strengths includes applying a predetermined function to the data relating to the link strengths in the user personality profiles.

17. The method according to claim 1, wherein

wherein at least a plurality of the link strengths are a function of both content-based and collaborative interrelationships between the nodes in the data network.

18. The method according to claim 1, further comprising

receiving, at the at least one server, review data for the plurality of venues, the review data reflecting an affinity of a plurality of reviewers for the plurality of venues, wherein

the data network further comprises nodes corresponding to the plurality of reviewers, and

at least a plurality of the link strengths are further a function of the review data.

19. The method according to claim 1, wherein

the plurality of venues include at least one of media content, consumer products, events, and experiences.

20. A method of providing venue recommendations on a client device, the method comprising:

transmitting, from the client device to at least one server, attribute data for a plurality of users, the attribute data relating to a plurality of attributes of the plurality of users and to one or more venues for which the plurality of users has an affinity;

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

receiving, from the at least one server based on the recommendation request, recommendation data identifying one or more recommended venues, each recommended venue being selected based on at least one venue attribute and the strength of a nodal interrelationship between a plurality of venues and a combined personality profile; and

displaying, on a screen of the client device, the recommendation data identifying the at least one recommended venue, wherein

the nodal interrelationships are developed within a data network comprising nodes corresponding at least to the plurality of venues that includes the one or more venues for which the plurality of users have the affinity and the plurality of users, and further comprising links between said nodes, each link reflecting a strength of an interrelationship between at least two nodes,

the combined personality profile is calculated based on user personality profiles calculated from the data network for at least a first user and a second user of the plurality of users, wherein the user personality profiles each include data relating to the link strengths between the first user and the one or more venues for which the first user has the affinity and the second user and the one or more venues for which the second user has the affinity, and

the combined personality profile represents a combination of the data relating to the link strengths corresponding to the venue data of the one or more venues for which the at least first and second users have the affinity.

21. The method according to claim 20, wherein:

the user personality profiles further include data relating to the link strength between the at least first and second users, and

the combined personality profile further represents a combination of the data relating to the link strength between the at least first and second users.

22. The method according to claim 21, wherein

at least a plurality of the link strengths are a function of at least the review data and the venue data.

23. The method according to claim 22, wherein:

the at least one server calculates weights corresponding to the at least first and second users'"'"' affinity for each venue attribute of one or more venues, of the plurality of venues, for which the at least first and second users have the affinity, and

the at least one server determines, based on the weights, an overall score for each venue attribute in the at least first and second users'"'"' user personality profiles.

24. The method according to claim 23, wherein the overall scores in the at least first and second users'"'"' user personality profiles are combined when calculating the combined personality profile.

25. The method according to claim 22, further comprising:

transmitting, from the client device to at the at least one server, filter conditions corresponding to at least one of the one or more venues for which the at least first and second users have the affinity and the one or more venue attributes of the one or more venues for which the at least first and second users have the affinity, wherein

the at least one server adjusts, based on the filter conditions, one or more of the user personality profiles, the recommendation data, and the combined personality profile.