A method, apparatus and computer-readable storage medium for determining one or more recommendations by applying efficient adaptive matrix factorization are disclosed. The method comprises causing, at least in part, an iterative performing of the following steps: a using of a current data set to optimize parameters used to adapt a current matrix factorization model by the end of the current time period, and a training of a current matrix factorization model and the current data set by the end of the current time period, based on the optimized parameters, to obtain an adapted matrix factorization model for service in a next time period.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method comprising: facilitating a processing of and/or processing data and/or information and/or at least one signal, the data and/or information and/or at least one signal based, at least in part, on the following: an iterative performing of a using of a current data set to optimize parameters used to adapt a current matrix factorization model by the end of the current time period, and a training of a current matrix factorization model and the current data set by the end of the current time period, based on the optimized parameters, to obtain an adapted matrix factorization model for service in a next time period. 2. A method of claim 1, wherein, for the parameters, the data and/or information and/or at least one signal are further based, at least in part, on the following: a training of the current data set to obtain a temp matrix factorization model; a splitting of the current data set into at least two parts, use one of the at least two parts for testing and using the rest for training, in order to obtain the parameters. 3. A method according to claim 2, wherein the data and/or information and/or at least one signal are further based, at least in part, on the following: wherein the adapted matrix factorization model is obtained by using the current matrix factorization model and the temp matrix factorization model. 4. A method according to claim 3, wherein the data and/or information and/or at least one signal are further based, at least in part, on the following: before the iterative performing, a training of an initial data set to an initial matrix factorization model; and a using of the initial matrix factorization model as a current matrix factorization model in the current time period. 5. A method according to claim 4, wherein the data and/or information and/or at least one signal are further based, at least in part, on the following: a deleting of the initial data set after the training of an initial data set to an initial matrix factorization model. 6. A method according to claim 1, wherein the data and/or information and/or at least one signal are further based, at least in part, on the following: a deleting of the current data set after obtaining the adapted matrix factorization model. 7. A method according to claim 1, wherein the data and/or information and/or at least one signal are further based, at least in part, on the following: wherein the current data set is a set of activity information collected in a current time period. 8. A method according to claim 1, wherein the data and/or information and/or at least one signal are further based, at least in part, on the following: wherein a using of the adapted matrix factorization model as recommendation model during the next time period. 9. A method according to claim 1, wherein the data and/or information and/or at least one signal are further based, at least in part, on the following: at least one advertisement based, at least in part, on the recommendation; activity information associated with one or more users with respect to the at least one advertisement, wherein the one or more users are associated with at least one user; and a presentation of the at least one advertisement to the at least one user, the at least one advertisement including an indication based, at least in part, on the activity information. 10. A method of claim 9, wherein the data and/or information and/or at least one signal are further based, at least in part, on the following: at least one determination of the one or more users associated with the at least one user based, at least in part, on one or more connections between the at least one user and the one or more users through one or more social networking sites, one or more websites, one or more organizations, or a combination thereof. 11. A method according to claim 9, wherein the data and/or information and/or at least one signal are further based, at least in part, on the following: a visualization of the indication based, at least in part, on at least one color, at least one symbol, at least one rating, at least one identifier corresponding to one or more of the one or more users, or a combination thereof. 12. A method according to claim 1, wherein the data and/or information and/or at least one signal are further based, at least in part, on the following: a using of the current data set to verify the current matrix factorization by the end of the current time period. 13. An apparatus comprising: at least one processor; and at least one memory including computer program code for one or more programs, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus to perform at least the following, cause, at least in part, an iterative performing of the following: a using of a current data set to optimize parameters used to adapt a current matrix factorization model by the end of the current time period, and a training of a current matrix factorization model and the current data set by the end of the current time period, based on the optimized parameters, to obtain an adapted matrix factorization model for service in a next time period. 14. An apparatus of claim 13, wherein the using of a current data set to optimize parameters comprises, at least in part, the apparatus being further caused to: train and/or facilitate a training of the current data set to obtain a temp matrix factorization model, by using the at least two train-test pairs; and split and/or facilitate a splitting of the current data set into at least two parts, use one of the at least two parts for testing and using the rest for training, in order to obtain the parameters. 15. An apparatus according to claim 14, wherein the apparatus is further caused to: obtain the adapted matrix factorization model by using the current matrix factorization model and the temp matrix factorization model. 16. An apparatus of claim 15, wherein the apparatus is further caused to: before the iterative performing, train and/or facilitate a training of an initial data set to an initial matrix factorization model; and use and/or facilitate a using of the initial matrix factorization model as a current matrix factorization model in the current time period. 17. An apparatus according to claim 13, wherein the apparatus is further caused to: delete and/or facilitate a deleting of the initial data set after the training of an initial data set to an initial matrix factorization model. 18. An apparatus of claim 13, wherein the apparatus is further caused to: delete and/or facilitate a deleting of the current data set after obtaining the adapted matrix factorization model. 19. An apparatus of claim 13, wherein the current data set is a set of activity information collected in a current time period. 20. An apparatus of claim 13, wherein the apparatus is further caused to: use and/or facilitate a using of the adapted matrix factorization model as recommendation model during the next time period. 21. An apparatus according to claim 13, wherein the apparatus is further caused to: determine at least one advertisement based, at least in part, on the recommendation; determine activity information associated with one or more users with respect to the at least one advertisement, wherein the one or more users are associated with at least one user; and cause, at least in part, a presentation of the at least one advertisement to the at least one user, the at least one advertisement including an indication based, at least in part, on the activity information. 22. An apparatus of claim 21 wherein the apparatus is further caused to: determine the one or more users associated with the at least one user based, at least in part, on one or more connections between the at least one user and the one or more users through one or more social networking sites, one or more websites, one or more organizations, or a combination thereof. 23. An apparatus according to claim 21, wherein the apparatus is further caused to: cause, at least in part, a visualization of the indication based, at least in part, on at least one color, at least one symbol, at least one rating, at least one identifier corresponding to one or more of the one or more users, or a combination thereof. 24. An apparatus according to claim 13, wherein the apparatus is further caused to: use and/or facilitate a using of the current data set to verify the current matrix factorization by the end of the current time period. 25. A method comprising: causing, at least in part, an iterative performing of: a using of a current data set to optimize parameters used to adapt a current matrix factorization model by the end of the current time period, and a training of a current matrix factorization model and the current data set by the end of the current time period, based on the optimized parameters, to obtain an adapted matrix factorization model for service in a next time period. 26. A method of claim 25, wherein the using of a current data set to optimize parameters comprises at least in part: a training of the current data set to obtain a temp matrix factorization model; a splitting of the current data set into at least two parts, use one of the at least two parts for testing and using the rest for training, in order to obtain the parameters. 27. A method according to claim 26, wherein the adapted matrix factorization model is obtained by using the current matrix factorization model and the temp matrix factorization model. 28. A method according to claim 27, before the iterative performing, a training of an initial data set to an initial matrix factorization model; and a using of the initial matrix factorization model as a current matrix factorization model in the current time period. 29. A method according to claim 25, further comprising: a deleting of the initial data set after the training of an initial data set to an initial matrix factorization model. 30. A method according to claim 25, further comprising: a deleting of the current data set after obtaining the adapted matrix factorization model. 31. A method according to claim 25, wherein the current data set is a set of activity information collected in a current time period. 32. A method according to claim 25, wherein a using of the adapted matrix factorization model as recommendation model during the next time period. 33. A method according to claim 25, further comprising: at least one advertisement based, at least in part, on the recommendation; activity information associated with one or more users with respect to the at least one advertisement, wherein the one or more users are associated with at least one user; and a presentation of the at least one advertisement to the at least one user, the at least one advertisement including an indication based, at least in part, on the activity information. 34. A method of claim 33, wherein at least one determination of the one or more users associated with the at least one user based, at least in part, on one or more connections between the at least one user and the one or more users through one or more social networking sites, one or more websites, one or more organizations, or a combination thereof. 35. A method according to claim 33, wherein a visualization of the indication based, at least in part, on at least one color, at least one symbol, at least one rating, at least one identifier corresponding to one or more of the one or more users, or a combination thereof. 36. A method according to claim 25, further comprising: a using of the current data set to verify the current matrix factorization by the end of the current time period. 37. An apparatus according to claim 13, wherein the apparatus is a mobile phone further comprising: user interface circuitry and user interface software configured to facilitate user control of at least some functions of the mobile phone through use of a display and configured to respond to user input; and a display and display circuitry configured to display at least a portion of a user interface of the mobile phone, the display and display circuitry configured to facilitate user control of at least some functions of the mobile phone. 38. 39. An apparatus of claim 37, wherein the apparatus is a mobile phone further comprising: user interface circuitry and user interface software configured to facilitate user control of at least some functions of the mobile phone through use of a display and configured to respond to user input; and a display and display circuitry configured to display at least a portion of a user interface of the mobile phone, the display and display circuitry configured to facilitate user control of at least some functions of the mobile phone. 40-45. (canceled)