Abstract: A method and a device for selecting data content to be pushed to a terminal are disclosed. In some embodiments, the method includes: acquiring a user identifier, and acquiring a characteristic value, corresponding to the user identifier, in a preset user attribute type; acquiring data content, and searching for a decision tree object corresponding to the data content; locating a leaf node corresponding to the user identifier in the decision tree object based on the characteristic value, corresponding to the user identifier, in the preset user attribute type; and acquiring the number of clicks and the number of pushes stored in the located leaf node, generating a selection reference value based on the number of clicks and the number of pushes, and selecting, based on the selection reference value, data content to be pushed to a terminal corresponding to the user identifier.

Abstract: A personal construction method and device are provided, the method includes: obtaining log information of a user; filtering the log information to obtain a key field of the log information; extracting a tag identifier of the key field; obtaining tag information based on the tag identifier and the key field, wherein the tag information comprises the number of times the tag identifier appears within a first predetermined time period; determining a category of interest corresponding to the identifier information based on the tag identifier; and obtaining a user interest degree corresponding to the category of interest based on the tag information.

Abstract: A method and a device for selecting data content to be pushed to a terminal are disclosed. In some embodiments, the method includes: acquiring a user identifier, and acquiring a characteristic value, corresponding to the user identifier, in a preset user attribute type; acquiring data content, and searching for a decision tree object corresponding to the data content; locating a leaf node corresponding to the user identifier in the decision tree object based on the characteristic value, corresponding to the user identifier, in the preset user attribute type; and acquiring the number of clicks and the number of pushes stored in the located leaf node, generating a selection reference value based on the number of clicks and the number of pushes, and selecting, based on the selection reference value, data content to be pushed to a terminal corresponding to the user identifier.

Abstract: A user behavior data analysis method and device, used to accurately analyze user behavior and make advertising more targeted.

Abstract: A personal construction method and device are provided, the method includes obtaining log information of a user; filtering the log information to obtain a key field of the information; extracting a tag identifier of the key field; obtaining tag information based on the tag identifier and the key field, wherein the tag information comprises the number of times the tag identifier appears within a first predetermined time period; determining a category of interest corresponding to the identifier information based on the tag identifier, and obtaining a user interest degree corresponding to the category of interest based on the tag information.

Abstract: The present disclosure provides a method and a device for advertisement classification, a server and a storage medium in the field of information technologies. The method includes: obtaining, according to text information of an advertisement to be classified, a plurality of feature words of the text information; acquiring a Term Frequency-Inverse Document Frequency value of each feature word from the plurality of feature words as a weight value of the feature word, according to statistical information of the feature word in the text information and statistical information of the feature word in known product titles; and acquiring a category of the advertisement according to the weight values of the plurality of feature words, classification information of the advertisement and a preset classification model. Accordingly, selecting the data from the advertisement in a manner of manual labeling is avoided, so that the time taken for advertisement classification is reduced.