Recommendation model training method and device, computer equipment and storage medium

Abstract

The invention discloses a recommendation model training method and device, computer equipment and a storage medium, and belongs to the technical field of computers. According to the method, the secondpreference information of the user for the unpurchased object is preliminarily predicted based on the first preference information of the user for the purchased object, and the features of the user dimension and the features of the object dimension are extracted from the obtained first preference information and second preference information respectively; then the currently acquired preference information based on the features of the two dimensions is corrected to obtain third preference information with higher accuracy, and the training data is generated according to the third preference information so that data modeling is not required to be directly performed according to original online data, and the complexity and time consumption of a training data generation process are reduced; therefore, the training period of the model is shortened, the model can be updated in time according to the latest user data, and the output result of the model can better accord with the current user preference.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a recommended model training method, device, computer equipment and storage medium. Background technique [0002] With the development of Internet technology, personalized recommendation of items based on cloud computing and big data is widely used. For example, it can be applied to the field of games to recommend various virtual items for users. At present, when recommending items, an item recommendation model based on a deep neural network is usually used. In the model training stage, user attribute features, item attribute features, and contextual features of user items need to be extracted from a large amount of online data. Data modeling, constructing a training data set to train the item recommendation model, so as to obtain a trained item recommendation model. [0003] However, in the above-mentioned model training process, the amount of data that needs to be ...

AI Technical Summary

Problems solved by technology

[0003] However, in the above-mentioned model training process, the amount of data that needs to be processed to construct the training data is huge. The current method of directly extracting features from online data for data modeling takes a long time, resulting in time-consuming model training. Long, it is difficult to meet the needs of rapid business launch

Moreover, because a model training takes a long time, it also leads to a long update cycle of model parameters. When the user's preferences for items change over time, the parameters of the recommended model will not be updated in time according to the current user data. The accuracy of the model prediction results is low

Images