User behavior model training method, recommending method, model training device and equipment

Abstract

The invention discloses a user behavior model training method, a recommending method, a model training device and equipment. The model training method comprises the steps of acquiring a user behaviorsequence; inputting the user behavior sequence into a user behavior sequence model under the current model parameter to obtain a current user expression; obtaining a first training sample according tothe current user expression and the user behavior sequence; determining a mutual information loss value by adopting a mutual information loss function according to the first training sample, and updating model parameters of the user behavior sequence model according to the mutual information loss value; and taking the updated model parameter as the current model parameter, and returning to execute the step of inputting the user behavior sequence into the user behavior sequence model under the current model parameter to obtain the current user expression until the current model parameter meetsthe preset condition. User behavior sequence modeling is realized through an unsupervised learning method based on mutual information maximization, the training time and cost of the user behavior sequence model are reduced, and the invention can be widely applied to the field of artificial intelligence.

Description

technical field [0001] The present application relates to the field of artificial intelligence, in particular to a user behavior model training and recommendation method, device and equipment. Background technique [0002] With the development of the Internet, users of e-commerce, social media and other products have grown exponentially. On the Internet, users behave in a variety of ways. For example, in social applications, users’ chatting behaviors, browsing behaviors of Moments, sending red envelopes, etc. can be obtained; in information applications, there are behaviors of clicking information, forwarding information, commenting, replying, and collecting behaviors, etc.; In search engines, there are search behaviors, click behaviors, etc. User behavior is an important feature to distinguish different user characteristics, and user behavior is time-series. Therefore, how to model the user behavior sequence over time is a very important research hotspot. [0003] In re...

AI Technical Summary

Problems solved by technology

However, this method requires high manual labeling costs, and in order to obtain better model performance, it is often necessary to constr

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