Systems and methods are provided for building a user model. The system includes a processor and a non-transitory storage medium accessible to the processor. The processor is configured to obtain user data from a database, where the user data include user behavior for a plurality of apps installed on one or more user terminals. The processor selects at least one rating parameters using the user data, where the at least one rating parameters indicates a rating of relevant app usage. The system builds the user model based on a rating matrix comprising the at least one rating parameters.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system for building a user model, comprising: a processor and a non-transitory storage medium accessible to the processor, the processor configured to: obtain user data from a database, wherein the user data comprise user behavior for a plurality of apps installed on one or more user terminals; select at least one rating parameters using the user data, wherein the at least one rating parameters indicates a rating of relevant app; and build the user model based on a rating matrix comprising the at least one rating parameters. 2. The system of claim 1, wherein at least one rating parameters comprises at least one of the following: an explicit rating by a user; and an implicit rating, the implicit rating comprising usage time that represents time spent on an app in a preset time period and interaction frequency indicating a frequency of accessing the app. 3. The system of claim 1, wherein at least one rating parameters comprises: normalized usage time that represents a ratio of the usage time compared with aggregate app statistics for a group of users with a preset common character. 4. The system of claim 1, wherein at least one rating parameters comprises: log usage time that represents a log transformation to account for marginal utility. 5. The system of claim 1, wherein the processor is configured to build the user model based on the rating matrix using matrix factorization. 6. The system of claim 1, wherein the processor is configured to build the user model using collaborative filtering. 7. The system of claim 5, wherein the processor is configured to project the rating matrix into a product of a first factor matrix U and a second factor matrix P. 8. The system of claim 7, wherein the processor is configured to: introduce a weight matrix W to calculate a cost function of weighted least square errors; build the user model by minimizing the cost function via alternatively estimating the first factor matrix U using the second factor matrix P and estimating the second factor matrix P using the first factor matrix U; estimate app usage using the user model; and recommend at least one app candidates based on the estimated app usage. 9. A method for building a user model, comprising: obtaining, by one or more devices having a processor, user data from a database, wherein the user data comprise user behavior for a plurality of apps installed on one or more user terminals; selecting, by the one or more devices, at least one rating parameters using the user data, wherein the at least one rating parameters indicates a rating of relevant app; building, by the one or more devices, the user model based on a rating matrix comprising the at least one rating parameters; estimating, by the one or more devices, app usage using the user model; and recommending, by the one or more devices, at least one app candidates based on the app usage. 10. The method of claim 9, wherein at least one rating parameters comprises at least one of the following: an explicit rating by a user; and an implicit rating, the implicit rating comprising usage time that represents time spent on an app in a preset time period and interaction frequency indicating a frequency of accessing the app. 11. The method of claim 9, wherein at least one rating parameters comprises: normalized usage time that represents a ratio of the usage time compared with aggregate app statistics for a group of users with a preset common character. 12. The method of claim 9, wherein at least one rating parameters comprises: log usage time that represents a log transformation to account for marginal utility. 13. The method of claim 9, further comprising: building the user model based on the rating matrix using matrix factorization. 14. The method of claim 13, wherein the matrix factorization comprises collaborative filtering. 15. The method of claim 13, further comprising: projecting the rating matrix into a product of a first factor matrix U and a second factor matrix P. 16. The method of claim 15, further comprising: introducing a weight matrix W to calculate a cost function of weighted least square errors; and building the user model by minimizing the cost function via alternatively estimating the first factor matrix U using the second factor matrix P and estimating the second factor matrix P using the first factor matrix U. 17. A non-transitory storage medium, comprising: instructions executable to obtain user data from a database, wherein the user data comprise user behavior for a plurality of apps installed on one or more user terminals; instructions executable to select at least one rating parameters using the user data, wherein the at least one rating parameters indicates a rating of relevant app; and instructions executable to build a user model based on a rating matrix comprising the at least one rating parameters. 18. The non-transitory storage medium of claim 17, further comprising: instructions executable to build the user model based on the rating matrix using collaborative filtering; wherein at least one rating parameters comprises at least one of the following: usage time that represents time spent on an app in a preset time period; normalized usage time that represents a ratio of the usage time compared with aggregate app statistics for a group of users with a preset common character; and log usage time that represents a log transformation to account for marginal utility. 19. The non-transitory storage medium of claim 17, further comprising: instructions executable to project the rating matrix into a product of a first factor matrix U and a second factor matrix P. 20. The non-transitory storage medium of claim 19, further comprising: instructions executable to introduce a weight matrix W to calculate a cost function of weighted least square errors; instructions executable to build the user model by minimizing the cost function via alternatively estimating the first factor matrix U using the second factor matrix P and estimating the second factor matrix P using the first factor matrix U; instructions executable to estimate app usage using the user model; and instructions executable to recommend at least one app candidates based on the estimated app usage.