Systems and methods to determine location recommendations

A system comprising:

• 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.

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:

• 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.