Personalized recommendations based on item usage
First Claim
1. A method of recommending personalized computer applications to users, the method comprising:
- receiving usage data collected from a computing device of a user, the usage data reflecting a degree of usage of user applications installed on the computing device of the user, the degree of usage reflecting a selected usage pattern that indicates how often the user applications were used;
identifying one or more related applications that are related to the user applications by at least comparing the usage data for the user to usage of the one or more related applications by other users, said identifying comprising identifying an application that was used with a similar usage pattern to the selected usage pattern;
weighting the user applications in the usage data based at least in part on the degree of usage of each application, such that the user applications used more frequently by the user are weighted more heavily than the user applications used less frequently by the user; and
selecting a subset of the related applications as recommendations for the user based at least in part on the weightings, thereby filtering the recommendations based on the degree of usage of the user applications;
wherein at least said identifying is implemented by one or more processors.
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Abstract
This disclosure describes systems and associated processes for generating recommendations for users based on usage, among other things. These systems and processes are described in the context of an interactive computing system that enables users to download applications for mobile devices (such as phones) or for other computing devices. Users'"'"' interactions with applications once they are downloaded can be observed and tracked, with such usage data being collected and provided to the interactive computing system. The interactive computing system can include a recommendation system or service that processes the usage data from a plurality of users to detect usage patterns. Using these usage patterns, among possibly other data, the recommendation system can recommend applications to users for download.
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Citations
16 Claims
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1. A method of recommending personalized computer applications to users, the method comprising:
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receiving usage data collected from a computing device of a user, the usage data reflecting a degree of usage of user applications installed on the computing device of the user, the degree of usage reflecting a selected usage pattern that indicates how often the user applications were used; identifying one or more related applications that are related to the user applications by at least comparing the usage data for the user to usage of the one or more related applications by other users, said identifying comprising identifying an application that was used with a similar usage pattern to the selected usage pattern; weighting the user applications in the usage data based at least in part on the degree of usage of each application, such that the user applications used more frequently by the user are weighted more heavily than the user applications used less frequently by the user; and selecting a subset of the related applications as recommendations for the user based at least in part on the weightings, thereby filtering the recommendations based on the degree of usage of the user applications; wherein at least said identifying is implemented by one or more processors. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. Non-transitory physical storage comprising computer-executable instructions configured to implement a method of recommending personalized computer applications to users, the method comprising:
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receiving usage data associated with a computing system of a user, the usage data reflecting a degree of usage of a plurality of user applications installed on the computing system; identifying a subset of the user applications that have relatively higher usage by the user than others of the user applications, wherein said identifying the subset of user applications comprises identifying any of the user applications that have more than one usage by the user; identifying, by one or more computing devices, one or more related applications related to the subset of user applications, wherein said identifying the one or more related applications comprises accessing the one or more related applications from a data set that relates applications based at least partly on an analysis of application selection behavior by a plurality of users; and weighting the user applications in the usage data based at least in part on the degree of usage of each application, such that the user applications used more frequently by the user are weighted more heavily than the user applications used less frequently by the user; selecting a subset of the related applications as recommendations for the user based at least in part on the weightings, thereby selecting the recommendations based on the degree of usage of the user applications. - View Dependent Claims (12, 13)
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14. A system for recommending personalized computer applications to users, the system comprising:
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an application distribution service configured to provide applications for download by a plurality of user computing systems; and a recommendation service comprising computer hardware, the recommendation service comprising a usage recommender configured to; receive usage data associated with a selected computing system of a selected user, the usage data reflecting a degree of usage of a plurality of user applications installed on the computing system, the user applications being obtained from the application distribution service; identify a subset of the user applications that have relatively higher usage by the user than others of the user applications; identify, by one or more computing devices, one or more related applications related to the subset of user applications by at least comparing the usage data for the subset of user applications to usage of the one or more related applications by other users, said comparing comprising performing one or more of the following;
identifying a first application of the subset of user applications that was used at a similar frequency as a selected one of the one or more user applications and identifying a second application of the subset of user applications that was used at a related location as a selected one of the one or more user applications;apply weights to the user applications in the usage data based at least in part on the degree of usage of each application, such that the user applications used more frequently by the user are weighted more heavily than the user applications used less frequently by the user; and select a subset of the related applications as recommendations for the user based at least in part on the weights to thereby select the recommendations based on the degree of usage of the user applications. - View Dependent Claims (15, 16)
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Specification