Recommendation model training method and prediction method and device based on recommendation model

Abstract

The invention provides a recommendation model training method and a prediction method and device based on a recommendation model, and the method comprises the steps: obtaining at least one piece of sample data, and obtaining at least one feature subset according to a feature set of each piece of sample data; determining a condition corresponding to each feature subset in the condition set according to the attribute of each feature subset; wherein the condition set comprises at least two conditions, the at least two conditions respectively indicate different attributes of the feature subsets, and the attribute of each feature subset is consistent with the attribute indicated by the condition corresponding to each feature subset; and training a recommendation model corresponding to a condition corresponding to each feature subset in the model set by using each feature subset and the tag corresponding to each feature subset. By implementing the embodiment of the invention, a better recommendation model can be trained, and the prediction accuracy of the recommendation content is improved.

Description

technical field [0001] The present invention relates to the technical field of content recommendation, in particular to a recommendation model training method, a prediction method and a device based on a recommendation model. Background technique [0002] With the development of Internet technology and the rapid growth of information, how to quickly and effectively screen information so as to accurately recommend personalized content suitable for users (such as commodities, advertisements, news, APP, etc.) an important research topic. In order to solve this problem, a recommendation system based on Feature Engineering came into being. The recommendation system can perform machine learning based on existing user information, and then recommend personalized content that users may be interested in. Among them, the existing user information includes user characteristics (such as gender, age, city, hobbies, etc.), product characteristics (such as product type, content, display ...

AI Technical Summary

Problems solved by technology

However, there are many recommendation models at present, and different recommendation models have different abilities to solve problems. It is still a challenging problem to choose which recommendation model to solve the feature combination problem of current products to further improve the accuracy of personalized content recommendation. problem

Images