Method and device for recommending target object to user

Abstract

The invention provides a method for recommending a target object to a user. The method comprises that: an object feature of the target object is generated according to at least one of an object control feature and an object random feature of the target object; the object control features and the object random features are obtained after multi-task alternate training is performed on a control matching model and a random matching model; in the multi-task alternate training, a control sample is used as an input sample of a control matching model, a random sample is used as an input sample of a random matching model, and an optimization target is achieved by modifying an object control feature of a target object in the control sample or modifying a value of an object random feature of the target object in the random sample, wherein the optimization target comprises that the difference between the object control feature and the object random feature of the same target object is as large aspossible; and the user features and the object features of the target object are input into a matching model, and the target object recommended to the user is determined according to the matching degree of the user features and the object features output by the matching model.

Description

technical field [0001] This description relates to the technical field of data processing, and in particular to a method and device for recommending target objects to users. Background technique [0002] An important application of data mining is to use historical data to make predictions about the future. For example, predict tomorrow's weather through the weather data of the past several years, predict new movies that the user may be interested in through the movies he has watched before, and so on. The recommendation system usually predicts the degree of matching between the user and the target object based on the historical behavior of the user, so as to recommend the target object with a high degree of matching to the user. [0003] After using the prediction results of the recommender system to recommend the target object to the user, the user's behavior will change. The user’s feedback on the output results of the recommender system will be different from the user’s...

AI Technical Summary

Problems solved by technology

In this way, when the recommendation system continues to use the user behavior data based on the recommendation strategy to make predictions, the behavior data changed by the recommendation system will inevitably cause deviations in the prediction of the recommendation system, thereby reducing the accuracy of the prediction of the recommendation system. waste of time and energy

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