Abstract: One embodiment sets forth technique for computing a similarity score between two digital items is computed based on interaction histories associated with global users and interaction histories associated with local users. Global counts indicating the number of interactions associated with each unique pair of digital items are weighted based on a mixing rate. The weighted global counts are then combined with local counts to compute total counts. An effective interaction probability indicating the likelihood of a user interacting with one digital item in the pair of digital items after interacting with the other digital item in the pair is computed based on the total counts. The effective interaction probability is then corrected for noise, resulting in a similarity score indicating the similarity between the pair of digital items.

Abstract: One embodiment sets forth technique for computing a similarity score between two digital items is computed based on interaction histories associated with global users and interaction histories associated with local users. Global counts indicating the number of interactions associated with each unique pair of digital items are weighted based on a mixing rate. The weighted global counts are then combined with local counts to compute total counts. An effective interaction probability indicating the likelihood of a user interacting with one digital item in the pair of digital items after interacting with the other digital item in the pair is computed based on the total counts. The effective interaction probability is then corrected for noise, resulting in a similarity score indicating the similarity between the pair of digital items.

Abstract: One embodiment sets forth technique for computing a similarity score between two digital items is computed based on interaction histories associated with global users and interaction histories associated with local users. Global counts indicating the number of interactions associated with each unique pair of digital items are weighted based on a mixing rate. The weighted global counts are then combined with local counts to compute total counts. An effective interaction probability indicating the likelihood of a user interacting with one digital item in the pair of digital items after interacting with the other digital item in the pair is computed based on the total counts. The effective interaction probability is then corrected for noise, resulting in a similarity score indicating the similarity between the pair of digital items.

Abstract: One embodiment of the invention sets forth a mechanism for identifying similar items within a plurality of items available to a group of users. The mechanism includes identifying a first count that indicates a number of times any user has interacted with a first item included in the plurality of items and subsequently with any other item, identifying a second count that indicates a number of times any user within the has interacted with a second item included in the plurality of items after interacting with the first item, computing a probability that indicates the likelihood of a particular user interacting with the second item after interacting with the first item based on the first count and the second count, and computing a similarity score that indicates the similarity between the first item and the second item based on the probability and a popularity score associated with the second item.

Abstract: One embodiment sets forth technique for computing a similarity score between two digital items is computed based on interaction histories associated with global users and interaction histories associated with local users. Global counts indicating the number of interactions associated with each unique pair of digital items are weighted based on a mixing rate. The weighted global counts are then combined with local counts to compute total counts. An effective interaction probability indicating the likelihood of a user interacting with one digital item in the pair of digital items after interacting with the other digital item in the pair is computed based on the total counts. The effective interaction probability is then corrected for noise, resulting in a similarity score indicating the similarity between the pair of digital items.

Abstract: One embodiment of the invention sets forth a mechanism for identifying similar items within a plurality of items available to a group of users. The mechanism includes identifying a first count that indicates a number of times any user has interacted with a first item included in the plurality of items and subsequently with any other item, identifying a second count that indicates a number of times any user within the has interacted with a second item included in the plurality of items after interacting with the first item, computing a probability that indicates the likelihood of a particular user interacting with the second item after interacting with the first item based on the first count and the second count, and computing a similarity score that indicates the similarity between the first item and the second item based on the probability and a popularity score associated with the second item.