In accordance with an embodiment, described herein is a system and method for logistic matrix factorization of implicit feedback data, with application to media environments or streaming services. While users interact with an environment or service, for example a music streaming service, usage data reflecting implicit feedback can be collected in an observation matrix. A logistic function can be used to determine latent factors that indicate whether particular users are likely to prefer particular items. Exemplary use cases include providing personalized recommendations, such as personalized music recommendations, or generating playlists of popular artists.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for logistic matrix factorization of implicit feedback data, comprising: a computer including a processor and a memory; a set of usage data describing usage by users of items, and comprising an observation matrix, wherein each entry in the observation matrix represents the number of times that a particular user has interacted with a particular item; and an implicit matrix factorization logic which applies a logistic function to determine latent factors that indicate whether particular users are likely to prefer particular items, including factorizing the observation matrix by lower-dimensional matrices representing, respectively a user's taste, and an item's implicit characteristics; and wherein the system can be configured to determine recommended items for a user. 2. The system of claim 1, wherein the system is used with a media environment or streaming service that enables streaming of media content from a media server to a media device. 3. The system of claim 2, wherein the items are songs, music, movies, or other media content items. 4. The system of claim 3, wherein the implicit characteristics includes a style or genre of the songs, music, movies, or other media content items. 5. The system of claim 1, wherein the logistic function is trained using parallel processing by sharding the observation matrix and user and item blocks. 6. A method for providing logistic matrix factorization of implicit feedback data, comprising: providing, at a computer, a set of usage data describing usage by users of items, and comprising an observation matrix, wherein each entry in the observation matrix represents the number of times that a particular user has interacted with a particular item; and applying a logistic function to determine latent factors that indicate whether particular users are likely to prefer particular items, including factorizing the observation matrix by lower-dimensional matrices representing, respectively a user's taste, and an item's implicit characteristics; and wherein the method can be configured to determine recommended items for a user. 7. The method of claim 6, wherein the system is used with a media environment or streaming service that enables streaming of media content from a media server to a media device. 8. The method of claim 7, wherein the items are songs, music, movies, or other media content items. 9. The method of claim 8, wherein the implicit characteristics includes a style or genre of the songs, music, movies, or other media content items. 10. The method of claim 6, wherein the logistic function is trained using parallel processing by sharding the observation matrix and user and item blocks. 11. A non-transitory computer readable storage medium, including instructions stored thereon which when read and executed by one or more computers cause the one or more computers to perform the steps comprising: providing, at a computer, a set of usage data describing usage by users of items, and comprising an observation matrix, wherein each entry in the observation matrix represents the number of times that a particular user has interacted with a particular item; and applying a logistic function to determine latent factors that indicate whether particular users are likely to prefer particular items, including factorizing the observation matrix by lower-dimensional matrices representing, respectively a user's taste, and an item's implicit characteristics; and wherein the method can be configured to determine recommended items for a user. 12. The non-transitory computer readable storage medium, of claim 11, wherein the system is used with a media environment or streaming service that enables streaming of media content from a media server to a media device. 13. The non-transitory computer readable storage medium, of claim 12, wherein the items are songs. 14. The non-transitory computer readable storage medium, of claim 13, wherein the implicit characteristics includes a style or genre of the songs, music, movies, or other media content items. 15. The non-transitory computer readable storage medium, of claim 11, wherein the logistic function is trained using parallel processing by sharding the observation matrix and user and item blocks.