Model training method and device and recommendation information selection method and device

Abstract

The embodiment of the invention provides a model training method and device and a recommendation information selection method and device. The model training method comprises the steps: obtaining the scene characteristics and a historical recommendation list of a historical user, wherein the historical recommendation list comprises a plurality of historical recommendation items which are arranged according to a time sequence, and the real feedback values of all the historical recommendation items; associating the scene feature, the real feedback value of the first historical recommendation item and the second historical recommendation item to construct a training sample, wherein the second historical recommendation item is located in the next time sequence of the first historical recommendation item; and training the initial model by using a plurality of training samples to obtain a prediction model which is used for obtaining a prediction feedback value of the recommendation item. According to the embodiment of the invention, the real feedback value of the previous time sequence historical recommendation item and the next time sequence historical recommendation item are used as the training sample training model; therefore, rapid adaptive learning can be realized based on feedback of the previous time sequence, and a model for accurately obtaining the prediction feedback value of the recommendation item can still be obtained under the condition that the training sample size is small.

Description

technical field [0001] The present invention relates to the technical field of the Internet, in particular to a method and device for model training and selection of recommended information. Background technique [0002] In the Internet age of information overload, personalized recommendation has been valued by academia and industry. Personalized recommendation means that users do not need to provide clear needs, by analyzing the user's historical behavior to model the user's interests, so as to actively recommend information to users that can meet their interests and needs, and get recommendation feedback from the user's actual behavior . However, the interests of different users vary greatly, and their behavior patterns are so diverse that it is difficult to generalize the personalities of all users with only one model. And when the number of training samples of users is small, it is difficult for the model to quickly and accurately model different users. Contents of t...

AI Technical Summary

Problems solved by technology

However, the interests of different users vary widely, and their behavior patterns are so diverse that it is difficult to generalize the personalities of all users with only one model.

And when the user's training sample size is small, it is difficult for the model to quickly and accurately model different users

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