Article recommendation method and device, meta-network processing method and device, storage medium and equipment

Abstract

The invention relates to an article recommendation method and device, a meta-network processing method and device, a storage medium and equipment. The article recommendation method comprises the stepsof obtaining attribute information of an article and an object identifier of an article interaction object; performing feature extraction on the attribute information through a meta-stretching network to obtain a stretching vector; performing feature extraction on the object identifier through a meta-offset network to obtain an offset vector; performing feature intersection on the vectorized article identifier of the article and the stretching vector, and fusing the obtained intersection feature vector with the offset vector to obtain a fused feature vector; determining a recommendation probability based on the fusion feature vector, the vectorized attribute information and vectorized object information of the target object; and recommending the article to the target object when the recommendation probability meets a recommendation condition. By adopting the method, the accuracy of article recommendation can be improved, so that the effect of accurate recommendation is achieved.

Description

technical field [0001] The present application relates to the technical field of artificial intelligence, in particular to an item recommendation method, a meta-network processing method, a device, a storage medium and a device. Background technique [0002] With the continuous development of Internet technology, users can obtain various items from the Internet, such as application programs or other service products. For item providers, it is necessary to accurately recommend the developed new items to users in need to increase product usage. [0003] Since the developed new items are usually only used by a small number of users, when recommending, the traditional recommendation scheme not only recommends interested and mature items to users, but also pushes the new items to users to improve the use of new items Rate. However, in the above recommendation scheme, the user is interested in the mature item, but not necessarily interested in the new item, resulting in low accu...

AI Technical Summary

Problems solved by technology

However, in the above recommendation scheme, the user is interested in the mature item, but not necessarily interested in the new item, resulting in low accuracy in recommending items to the user

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