SYSTEMS AND METHODS FOR PROVIDING ENHANCED NEURAL NETWORK GENESIS AND RECOMMENDATIONS

• US 20140129371A1
• Filed: 11/05/2012
• Published: 05/08/2014
• Est. Priority Date: 11/05/2012
• Status: Abandoned Application

• Current Assignee: Nara Logics, Inc.
• Original Assignee: Nara Logics, Inc.
• Inventors: Wilson, Nathan R., Hueske, Emily A., Copeman, Thomas C.
• Application Number:
  US13/669,150
• Publication Number:
  US 20140129371A1
• Time in Patent Office: Days
• US Class Current: 705/26.7
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: G06Q 30/0631 Item recommendations

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 which are enhanced by dynamic resonance between source sites. The recommendation engine in certain embodiments determines recommended venues based on user attributes and venue preferences by performing geometric contextualization on generated recommendation sets and determining recommendation resonance with past recommendations. Remote businesses may also link with the recommendation generator to receive recommendations custom-tailored to their business. In selected embodiments, interconnectivity augmentation provides for enhanced neural network topology and recommendations for foreign locales. Various user interfaces are also contemplated thereby providing users with a view of the neural network topology as well as the ability to collaboratively determine meeting places.

Claims

1. A method implemented by at least one hardware server, the method comprising:

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

receiving, at the at least one hardware server,

local venue data for a plurality of venues of a user locale, the local venue data relating to a plurality of attributes of the venues, and

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

receiving, at the at least one hardware server, review data for the plurality of venues of the user locale and destination locale, the review data reflecting the affinity of a plurality of reviewers for the plurality of venues,

accessing, via the at least one hardware server, a data network having nodes corresponding at least to the plurality of venues of the user locale and user destination and the plurality of reviewers and further having 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 local review data and the local venue data and are further a function of both content-based and collaborative interrelationships;

performing, at the at least one hardware server, interconnectivity augmentation between a plurality of venues of the user locale and the plurality of venues of the user destination to form specific links therebetween, wherein the interconnectivity augmentation forms the specific links in part by identifying collaborative interrelationships between one or more venues of the user local and one or more venues of the user destination, wherein at least some of the collaborative interrelationships are a function of local venues and user destination venues having ratings from the same reviewer;

determining, at the at least one hardware server and based on the interconnectivity augmentation, a plurality of recommended venues out of the plurality of venues of the user destination, the plurality of recommended venues being further based on the link strengths and at least one venue parameter;

generating, at the at least one hardware server, recommendation data having at least one of the recommended venues; and

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

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

3. The method of claim 1, wherein the user locale identifies a location of the user in which the data network contains a predetermined number of links between nodes corresponding to the plurality of venues in the location.

4. The method of claim 1, wherein the user destination identifies a destination location the user is visiting in which the data network does not contain a predetermined number of links between nodes corresponding to the plurality of venues in the destination location.

7. The method of claim 1, where in the interconnectivity augmentation is performed between a plurality of venues of the user locale and the plurality of venues of the user destination based on the plurality of attributes of the venues of the user locale and user destination.

8. The method of claim 7, wherein the interconnectivity augmentation forms the specific links and determines corresponding link strengths between the plurality of venues of the user local and user destination by calculating a congruency factor between venues of the user locale and user destination with respect to the plurality of attributes therebetween.

9. The method of claim 8, wherein the congruency factor is determined based on the amount of corresponding attributes between the at least a first venue for which the user has an affinity and the venues of the user destination.

10. The method of claim 1, further comprising:

performing geometric contextualization to rank the plurality of recommended venues based on the number of recommended venues, a quality of the recommended venues, and a diversity of the recommended venues; and

generating, at the at least one hardware server, recommendation data having at least one of the recommend venues in an order based on the rank of the recommended venues.

11. The method of claim 1, further comprising:

serving a user interface depicting a query node representing the first venue, a plurality of nodes having interrelationships with the at least first venue and the corresponding links therebetween,

wherein a thickness of the depicted links is a function of the link strengths between the query node and the plurality of nodes.

12. The method of claim 1, further comprising:

storing, in a database, a plurality of previous recommendation data having a plurality of recommended venues stored in correspondence with at least one of user attribute data, venue attribute data, and local review data;

comparing, at the at least one hardware server, the recommendation data to the plurality of previous recommendation data to generate a resonance quantifier identifying a level of resonance between the recommendation data and the plurality of previous recommendation data;

storing, in response to the resonance quantifier being greater than a predetermined threshold, the recommendation data in the database; and

serving only the recommendation data having a resonance quantifier greater than the predetermined threshold to the client device for display on the screen of the client device.

13. The method of claim 12, wherein a plurality of resonance values are calculated by comparing the user attribute data, venue attribute data and local review data of each recommended venue to user attribute data, venue attribute data and local review data of each of the plurality of previous recommendation data, and the resonance quantifier is determined based on each resonance value.

26. The method of claim 1, further comprising:

identifying one or more reviewers who have provided ratings to at least one venue of the user locale and at least one venue of the destination locale; and

generating, for each reviewer and based on the ratings, collaborative interrelationships between the at least one venue of the user local and the at least one venue of the destination locale.

5. (canceled)

6. The method of claim 5, wherein the interconnectivity augmentation determines corresponding link strengths between the plurality of venues of the user local and user destination in part by identifying a degree of similarity between ratings from the same reviewer.

14. A method implemented by at least one server, the method comprising:

receiving, at the at least one server, 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, at the at least one server, venue data for a plurality of venues, the venue data relating to a plurality of attributes of the venues;

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

accessing, via the at least one server, a data network having nodes corresponding at least to the plurality of venues and the plurality of reviewers and further having 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, are further a function of both content-based and collaborative interrelationships, and are based in part on higher order interrelationships between venues;

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

performing geometric contextualization to rank the recommended venues based on the number of recommended venues, a quality of the recommended venues, and a diversity of the recommended venues;

generating, at the at least one server, recommendation data having at least one of the recommend venues in an order based on the rank of the recommended venues; and

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

15. The method of claim 14, wherein the quality of the recommended venues is based on at least one of link strengths to the first venue, and user purchase data and user affinity data with respect to the recommended venues.

16. The method of claim 14, wherein the diversity of the recommended venues is based on a diversity factor representative of link strengths between the plurality of recommended venues and the amount of corresponding attributes between venue data of the plurality of recommended venues.

17. The method of claim 14, wherein the geometric contextualization is further based on a user distance function indicating a radial distance within which the plurality of recommended venues must be located.

18. The method of claim 14, further comprising:

serving a user interface depicting a query node representing the first venue, a plurality of nodes having interrelationships with the at least first venue and the corresponding links therebetween,

wherein a thickness of the depicted links is a function of the link strengths between the query node and the plurality of nodes.

19. The method of claim 14, further comprising:

storing, in a database, a plurality of previous recommendation data having a plurality of recommended venues stored in correspondence with at least one of user attribute data, venue attribute data, and review data;

comparing, at the at least one server, the recommendation data to the plurality of previous recommendation data to generate a resonance quantifier identifying a level of resonance between the recommendation data and the plurality of previous recommendation data;

storing, in response to the resonance quantifier being greater than a predetermined threshold, the recommendation data in the database; and

serving only the recommendation data having a resonance quantifier greater than the predetermined threshold to the client device for display on the screen of the client device.

20. The method of claim 19, wherein each attribute data of the recommendation data is compared to each attribute of the plurality of previously recommendation data to determine a resonance value of each attribute data, and the resonance quantifier is determined based on each resonance value of each attribute data.

21. A method implemented by at least one server, the method comprising:

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

receiving, at the at least one server, item data for a plurality of items, the item data relating to a plurality of attributes of the items;

receiving, at the at least one server, review data for the plurality of items, the review data reflecting the affinity of a plurality of reviewers for the plurality of items;

receiving, at the at least one server and via an application programming interface, a request from third party vendor, the request including external item data relating to at least one external item sold by the third party vendor, and information identifying a type of the at least one external item included in the request;

accessing, via the at least one server, a data network having nodes corresponding at least to the type of the at least one external item, the plurality of items and the plurality of reviewers, and further having 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 item data, are further a function of both content-based and collaborative interrelationships, and are based in part on higher order interrelationships between items;

determining, at the at least one server and based on the link strengths and the at least one external item, a plurality of recommended items having the same or similar type as the at least one external item;

generating, at the at least one server, recommendation data having at least one of the recommend items; and

serving the recommendation data to the third party vendor via the application programming interface.

22. The method of claim 21, further comprising:

receiving, via the application programming interface, a request from the third party vendor that includes

item data for a plurality of third party vendor items, the item data relating to a plurality of attributes of the third party vendor items items;

external attribute data for a plurality of third party vendor customers, the external attribute data relating to a plurality of attributes of the customers, and

external review data for the at least one external item, the external review data reflecting the affinity of a plurality of external reviewers for the plurality of external items; and

generating, in response to receiving the request, a third party vendor data network index having nodes corresponding at least to the third party vendor items and the plurality of external reviewers and further having 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 item data of the third party vendor items and the external review data, are further a function of both content-based and collaborative interrelationships, and are based in part on higher order interrelationships between items.

23. The method of claim 22, further comprising:

determining, based on the link strengths within the third party vendor data network index and at the least one external item, a plurality of recommended items having the same or similar type as the at least one external item;

generating, at the at least one server, recommendation data having at least one of the recommend items; and

serving the recommendation data to the third party vendor via the application programming interface.

24. The method of claim 22, further comprising:

serving the third party vendor data network index to the third party vendor via the application programming interface.

25. The method of claim 21, further comprising:

performing geometric contextualization on the plurality of recommended items to rank the recommended venues based on the number of recommended items, a quality of the recommended items, and a diversity of the recommended items; and

generating, at the at least one server, recommendation data having at least one of the recommend items in an order based on the rank of the recommended items.