Systems and methods to determine location recommendations
First Claim
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1. A computer-implemented method comprising:
- receiving, by a computing system, ratings, provided by at least a user and a set of other users through a social networking system, for a plurality of locations associated with a location type, wherein receiving the ratings comprises;
receiving a rating for at least a first location from the user and ratings for at least the first location from the set of other users; and
receiving ratings for a second location from the set of other users;
developing, by the computing system, a personalized model for the user, based on attributes associated with the location type and the ratings for the plurality of locations wherein the developing comprises;
calculating a pairwise difference between the rating for at least the first location from the user and each rating for at least the first location from each user in the set of other users to generate difference values, wherein the difference values are weighted by an exponential decay function based on a similarity between the rating from the user and each rating from each user in the set of other users;
generating a confidence interval based at least in part on the weighted difference values;
calculating an expected rating for the second location based on the ratings for the second location from the set of other users; and
applying the confidence interval to the expected rating, wherein the confidence interval is associated with an accuracy of the expected rating;
providing, by the computing system, the second location as a recommendation for the user based on the personalized model based at least in part on whether the confidence interval satisfies a threshold accuracy;
determining, by the computing system, that an actual rating received from the user for the second location does not fall within the confidence interval; and
developing, by the computing system, the personalized model further for the user based on the actual rating and the ratings for the second location from the set of other users.
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Abstract
Systems, methods, and non-transitory computer readable media are configured to receive ratings for a plurality of locations associated with a location type. The ratings are processed to develop a personalized model for a user to identify candidate locations for the user. At least one candidate location is provided as a recommendation for the user based on the personalized model.
18 Citations
18 Claims
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1. A computer-implemented method comprising:
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receiving, by a computing system, ratings, provided by at least a user and a set of other users through a social networking system, for a plurality of locations associated with a location type, wherein receiving the ratings comprises; receiving a rating for at least a first location from the user and ratings for at least the first location from the set of other users; and receiving ratings for a second location from the set of other users; developing, by the computing system, a personalized model for the user, based on attributes associated with the location type and the ratings for the plurality of locations wherein the developing comprises; calculating a pairwise difference between the rating for at least the first location from the user and each rating for at least the first location from each user in the set of other users to generate difference values, wherein the difference values are weighted by an exponential decay function based on a similarity between the rating from the user and each rating from each user in the set of other users; generating a confidence interval based at least in part on the weighted difference values; calculating an expected rating for the second location based on the ratings for the second location from the set of other users; and applying the confidence interval to the expected rating, wherein the confidence interval is associated with an accuracy of the expected rating; providing, by the computing system, the second location as a recommendation for the user based on the personalized model based at least in part on whether the confidence interval satisfies a threshold accuracy; determining, by the computing system, that an actual rating received from the user for the second location does not fall within the confidence interval; and developing, by the computing system, the personalized model further for the user based on the actual rating and the ratings for the second location from the set of other users. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A system comprising:
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at least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the system to perform; receiving ratings, provided by at least a user and a set of other users through a social networking system, for a plurality of locations associated with a location type, wherein receiving the ratings comprises; receiving a rating for at least a first location from the user and ratings for at least the first location from the set of other users; and receiving ratings for a second location from the set of other users; developing a personalized model for the user, based on attributes associated with the location type and the ratings for the plurality of locations, wherein the developing comprises; calculating a pairwise difference between the rating for at least the first location from the user and each rating for at least the first location from each user in the set of other users to generate difference values, wherein the difference values are weighted by an exponential decay function based on a similarity between the rating from the user and each rating from each user in the set of other users; generating a confidence interval based at least in part on the weighted difference values; calculating an expected rating for the second location based on the ratings for the second location from the set of other users; and applying the confidence interval to the expected rating, wherein the confidence interval is associated with an accuracy of the expected rating; providing the second location as a recommendation for the user based on the personalized model based at least in part on whether the confidence interval satisfies a threshold accuracy; determining that an actual rating received from the user for the second location does not fall within the confidence interval; and developing the personalized model further for the user based on the actual rating and the ratings for the second location from the set of other users. - View Dependent Claims (10, 11, 12, 13)
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14. A non-transitory computer-readable storage medium including instructions that, when executed by at least one processor of a computing system, cause the computing system to perform a method comprising:
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receiving ratings, provided by at least a user and a set of other users through a social networking system, for a plurality of locations associated with a location type, wherein receiving the ratings comprises; receiving a rating for at least a first location from the user and ratings for at least the first location from the set of other users; and receiving ratings for a second location from the set of other users; developing a personalized model for the user, based on attributes associated with the location type and ratings for the plurality of locations, wherein the developing comprises; calculating a pairwise difference between the rating for at least the first location from the user and each rating for at least the first location from each user in the set of other users to generate difference values, wherein the difference values are weighted by an exponential decay function based on a similarity between the rating from the user and each rating from each user in the set of other users; generating a confidence interval based at least in part on the weighted difference values; calculating an expected rating for the second location based on the ratings for the second location from the set of other users; and applying the confidence interval to the expected rating, wherein the confidence interval is associated with an accuracy of the expected rating; providing the second location as a recommendation for the user based on the personalized model based at least in part on whether the confidence interval satisfies a threshold accuracy; determining that an actual rating received from the user for the second location does not fall within the confidence interval; and developing the personalized model further for the user based on the actual rating and the ratings for the second location from the set of other users. - View Dependent Claims (15, 16, 17, 18)
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Specification