Existing methods for analyzing user behavior in multiple domains is relatively simple. A user close or similar to a given target user is sought for in different domains, so as to position the target user and generate content to be recommended to the user.
The existing technology has the following disadvantages:
The interpretability is poor, so that a correlation between behavior characteristics in different domains cannot be established;
There is too much human intervention, it is requisite to define the means for measuring the user distance and threshold; and
The expandability is poor, a model cannot be quickly established for, and recommendation cannot be made to a new based on the modeling result.