Patent application number | Description | Published |
20130080364 | SYSTEMS AND METHODS FOR PROVIDING RECOMMENDATIONS BASED ON COLLABORATIVE AND/OR CONTENT-BASED NODAL INTERRELATIONSHIPS - 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 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. | 03-28-2013 |
20130275511 | SYSTEMS AND METHODS FOR PROVIDING RECOMMENDATIONS BASED ON COLLABORATIVE AND/OR CONTENT-BASED NODAL INTERRELATIONSHIPS - 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. | 10-17-2013 |
20140129371 | SYSTEMS AND METHODS FOR PROVIDING ENHANCED NEURAL NETWORK GENESIS AND RECOMMENDATIONS - 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. | 05-08-2014 |
20140244562 | SYSTEMS AND METHODS FOR PROVIDING RECOMMENDATIONS BASED ON COLLABORATIVE AND/OR CONTENT-BASED NODAL INTERRELATIONSHIPS - 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. | 08-28-2014 |
20140279196 | SYSTEM AND METHODS FOR PROVIDING SPATIALLY SEGMENTED RECOMMENDATIONS - In certain implementations, data is spatially segmented into a variety of grids having particular keyed location data. Items of interest located within the boundaries of each grid are identified and stored in association with the grid location information. Data with respect to venue attributes is encoded and stored in association with corresponding grid location data. The system will identify a grid location based on a recommendation request or based on the user location and will generate a list of items of interest in that location and neighboring locations. This information is filtered based on the particularities of the user request to form a final filter set. User attribute weights are then applied to the final filter set to determine an overall score for each item of interest. Items of interest are then recommended to the user based on their overall score. | 09-18-2014 |
20140280226 | APPARATUS AND METHOD FOR PROVIDING HARMONIZED RECOMMENDATIONS BASED ON AN INTEGRATED USER PROFILE - 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. | 09-18-2014 |
20150066830 | SYSTEMS AND METHODS FOR PROVIDING RECOMMENDATIONS BASED ON COLLABORATIVE AND/OR CONTENT-BASED NODAL INTERRELATIONSHIPS - 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. | 03-05-2015 |