Abstract: A method for flexible coordinated operation of an urban distribution network and a watershed network, including: constructing a watershed network dynamic operation model; constructing a river water storage model a lake water storage model based on the watershed network dynamic operation model; constructing a distribution network linear alternating-current (AC) power flow model and an operating power-flow rate operation model of the pump stations to obtain a coordinated operation model of the urban distribution network and the watershed network; constructing a watershed-electricity composite sensitivity matrix; quantifying a time-varying adjustable power domain of each pump station through a pump station flexibility assessment method based on the watershed-electricity composite sensitivity matrix; constructing a power flow optimization model of the urban distribution network and a hydraulic energy flow optimization model of the watershed network; and performing electricity-water energy flow interactive optimiz

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for grouping users are provided. One embodiment of the methods includes: dividing a plurality of users targeted by the an advertisement candidate into a plurality of user buckets, wherein each of the plurality of user buckets is associated with a first conversion score; obtaining a trained prediction model corresponding to the advertisement, wherein the trained prediction model is able to predict a conversion score based at least on the first conversion score associated with a user bucket and a second conversion score associated with a group of user buckets comprising the user bucket; and constructing an optimization model using the trained prediction model, wherein an objective function of the optimization problem is to maximize a total conversion score with a grouping strategy determined by solving the optimization problem.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for personalizing offers are provided. One of the methods includes: collecting response data comprising one or more offers made to each of a plurality of users of a platform and one or more corresponding responses, wherein the one or more offers are from a group of offer choices; creating a training dataset comprising the collected response data and one or more features associated with each of the plurality of users; training a machine learning model using the training dataset, wherein the trained machine learning model is configured to predict the plurality of users' responses to future offers; obtaining a plurality of projected profits for the platform using the trained machine learning model, wherein each of the plurality of projected profits corresponds to making one of the group of the predetermined offers to one of the plurality of users.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for grouping users are provided. One embodiment of the methods includes: dividing a plurality of users targeted by the an advertisement candidate into a plurality of user buckets, wherein each of the plurality of user buckets is associated with a first conversion score; obtaining a trained prediction model corresponding to the advertisement, wherein the trained prediction model is able to predict a conversion score based at least on the first conversion score associated with a user bucket and a second conversion score associated with a group of user buckets comprising the user bucket; and constructing an optimization model using the trained prediction model, wherein an objective function of the optimization problem is to maximize a total conversion score with a grouping strategy determined by solving the optimization problem.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for personalizing offers are provided. One of the methods includes: collecting response data comprising one or more offers made to each of a plurality of users of a platform and one or more corresponding responses, wherein the one or more offers are from a group of offer choices; creating a training dataset comprising the collected response data and one or more features associated with each of the plurality of users; training a machine learning model using the training dataset, wherein the trained machine learning model is configured to predict the plurality of users' responses to future offers; obtaining a plurality of projected profits for the platform using the trained machine learning model, wherein each of the plurality of projected profits corresponds to making one of the group of the predetermined offers to one of the plurality of users.

Abstract: Textual information related to user information from user service information is identified. A layered matching is performed on the textual information based on preset background identification information in a preset list, wherein the layered matching includes different matching methods, and the preset list includes a plurality of entries storing different preset background identification information related to the user information. The user information is determined based on the layered matching.

Abstract: Textual information related to user information from user service information is identified. A layered matching is performed on the textual information based on preset background identification information in a preset list, wherein the layered matching includes different matching methods, and the preset list includes a plurality of entries storing different preset background identification information related to the user information. The user information is determined based on the layered matching.