Embodiments of the present invention disclose a push information pre-selecting method and apparatus. The method includes: based on historical push data of push information including a plurality of push information items, determining a feature for calculating a predicted value, and a weight corresponding to the feature; calculating a standard deviation of the feature; determining a fluctuation probability of the standard deviation; calculating the predicted value based on the weight, the standard deviation, and the fluctuation probability, the standard deviation and the fluctuation probability being used for calculating a fluctuation value for correcting the weight; based on the predicted value, selecting push information items satisfying a preset condition; and pushing the selected push information items to a target user.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A push information pre-selecting method, comprising: based on historical push data of push information including a plurality of push information items, determining, by a computing terminal including at least one processor, a feature for calculating a predicted value, and a weight corresponding to the feature; calculating, by the computing terminal, a standard deviation of the feature; determining, by the computing terminal, a fluctuation probability of the standard deviation; calculating, by the computing terminal, the predicted value based on the weight, the standard deviation, and the fluctuation probability, the standard deviation and the fluctuation probability being used for calculating a fluctuation value for correcting the weight; based on the predicted value, selecting, by the computing terminal, push information items satisfying a preset condition; and pushing, by the computing terminal, the selected push information items to a target user. 2. The method according to claim 1, further comprising: determining a fluctuation coefficient, wherein the fluctuation coefficient is used for limiting a value range of the fluctuation value, wherein the calculating the predicted value based on the weight, the standard deviation, and the fluctuation probability comprises: calculating the predicted value based on the weight, the standard deviation, the fluctuation probability, and the fluctuation coefficient. 3. The method according to claim 2, further comprising: determining a safety factor, wherein the safety factor is used for preventing an abnormal fluctuation value caused when the standard deviation is of a particular value or the standard deviation is not obtained, wherein the calculating the predicted value based on the weight, the standard deviation, and the fluctuation probability comprises: calculating the predicted value based on the weight, the standard deviation, the fluctuation probability, and the safety factor. 4. The method according to claim 3, wherein the calculating the predicted value based on the weight, the standard deviation, the fluctuation probability, and the safety factor comprises: calculating the predicted value Y by: y = 1 1 + e - ∑ ( w i + α  1 β + 1 σ i 2 * p )  x i wherein xi is a value of a feature i, wi is a weight of the feature i, α is the fluctuation coefficient, β is the safety factor, and σi is a standard deviation of the feature. 5. The method according to claim 1, wherein the determining a feature for calculating a predicted value, and a weight corresponding to the feature comprises: determining push information features for calculating the predicted value; and determining user features for calculating the predicted value. 6. The method according to claim 5, wherein the determining user features for calculating the predicted value comprises: determining reliability of the user features; and selecting certain user features from the user features for calculating the predicted value based on the reliability of the user features. 7. The method according to claim 5, wherein the determining user features for calculating the predicted value comprises: selecting one or more unprocessed user features from the user features as the certain user features for calculating the predicted value. 8. A push information pre-selecting apparatus, comprising: a memory storing instructions; and a processor coupled to the memory and, when executing the instructions, configured for: based on historical push data of push information including a plurality of push information items, determining a feature for calculating a predicted value, and a weight corresponding to the feature; calculating a standard deviation of the feature; determining a fluctuation probability of the standard deviation; calculating the predicted value based on the weight, the standard deviation, and the fluctuation probability, the standard deviation and the fluctuation probability being used for calculating a fluctuation value for correcting the weight; based on the predicted value, selecting push information items satisfying a preset condition; and pushing the selected push information items to a target user. 9. The apparatus according to claim 8, wherein the processor is further configured for: determining a fluctuation coefficient, wherein the fluctuation coefficient is used for limiting a value range of the fluctuation value, wherein the calculating the predicted value based on the weight, the standard deviation, and the fluctuation probability comprises: calculating the predicted value based on the weight, the standard deviation, the fluctuation probability, and the fluctuation coefficient. 10. The apparatus according to claim 9, wherein the processor is further configured for: determining a safety factor, wherein the safety factor is used for preventing an abnormal fluctuation value caused when the standard deviation is of a particular value or the standard deviation is not obtained, wherein the calculating the predicted value based on the weight, the standard deviation, and the fluctuation probability comprises: calculating the predicted value based on the weight, the standard deviation, the fluctuation probability, and the safety factor. 11. The apparatus according to claim 10, wherein the processor is further configured for: calculating the predicted value y by: y = 1 1 + e - ∑ ( w i + α  1 β + 1 σ i 2 * p )  x i wherein xi is a value of a feature i, wi is a weight of the feature i, α is the fluctuation coefficient, β is the safety factor, and σi is a standard deviation of the feature. 12. The apparatus according to claim 8, wherein the processor is further configured for: determining push information features for calculating the predicted value; and determining user features for calculating the predicted value. 13. The apparatus according to claim 12, wherein the processor is further configured for: determining reliability of the user features; and selecting certain user features from the user features for calculating the predicted value based on the reliability of the user features. 14. The apparatus according to claim 12, wherein the processor is further configured for: selecting one or more unprocessed user features from the user features as the certain user features for calculating the predicted value. 15. A non-transitory computer-readable storage medium containing computer-executable instructions for, when executed by one or more processors, performing a push information pre-selecting method, the method comprising: based on historical push data of push information including a plurality of push information items, determining a feature for calculating a predicted value, and a weight corresponding to the feature; calculating a standard deviation of the feature; determining a fluctuation probability of the standard deviation; calculating the predicted value based on the weight, the standard deviation, and the fluctuation probability, the standard deviation and the fluctuation probability being used for calculating a fluctuation value for correcting the weight; based on the predicted value, selecting push information items satisfying a preset condition; and pushing the selected push information items to a target user. 16. The non-transitory computer-readable storage medium according to claim 15, the method further comprising: determining a fluctuation coefficient, wherein the fluctuation coefficient is used for limiting a value range of the fluctuation value, wherein the calculating the predicted value based on the weight, the standard deviation, and the fluctuation probability comprises: calculating the predicted value based on the weight, the standard deviation, the fluctuation probability, and the fluctuation coefficient. 17. The non-transitory computer-readable storage medium according to claim 16, the method further comprising: determining a safety factor, wherein the safety factor is used for preventing an abnormal fluctuation value caused when the standard deviation is of a particular value or the standard deviation is not obtained, wherein the calculating the predicted value based on the weight, the standard deviation, and the fluctuation probability comprises: calculating the predicted value based on the weight, the standard deviation, the fluctuation probability, and the safety factor. 18. The non-transitory computer-readable storage medium according to claim 17, wherein the calculating the predicted value based on the weight, the standard deviation, the fluctuation probability, and the safety factor comprises: calculating the predicted value y by: y = 1 1 + e - ∑ ( w i + α  1 β + 1 σ i 2 * p )  x i wherein xi is a value of a feature i, wi is a weight of the feature i, α is the fluctuation coefficient, β is the safety factor, and σi is a standard deviation of the feature. 19. The non-transitory computer-readable storage medium according to claim 15, wherein the determining a feature for calculating a predicted value, and a weight corresponding to the feature comprises: determining push information features for calculating the predicted value; and determining user features for calculating the predicted value. 20. The non-transitory computer-readable storage medium according to claim 19, wherein the determining user features for calculating the predicted value comprises: determining reliability of the user features; and selecting certain user features from the user features for calculating the predicted value based on the reliability of the user features.