Content recommendation method, system and device and storage medium

Abstract

The invention discloses a content recommendation method, system and device and a storage medium, and relates to the field of artificial intelligence, and the method comprises the steps: representing the browsing information of an account through a unit of a third-order tensor based on a third-order tensor formed by triples of an account identifier, a content identifier and a label identifier, obtaining a first relation matrix from the decomposition of the third-order tensor, compared with matrix decomposition and third-order tensor decomposition and extracting more hidden information. The relations between the label and the content can be obtained from other tasks or through simple manual annotation so that acquisition cost is low, the first label feature of the target account can be obtained based on the first relation matrix, and the first label feature of the target account can be obtained based on the second relation matrix. According to the embodiment of the invention, the secondlabel feature of the first candidate content can be obtained based on the second relation matrix, and then the content interested by the user is determined based on the label feature, so that comparedwith the related technology, the embodiment of the invention has more decision information, and the content recommendation accuracy can be improved.

Description

technical field [0001] The present application relates to artificial intelligence technology, especially a content recommendation method, system, device and storage medium. Background technique [0002] In the content recommendation technology, artificial intelligence technology is usually used to select the content that the user is interested in from the massive content. However, based on the traditional neural network model, it is necessary to mark the user's click behavior, and a large amount of data is required to learn the user's interest, and it is difficult to obtain training data. However, the user's reading interest is more likely to change in the short term. Therefore, the cost of modeling using traditional neural network models is high, and the training time is often longer, so it is not easy to achieve high accuracy in content recommendation scenarios. . [0003] There is also a recommendation system that uses matrix decomposition. In related technologies, matr...

AI Technical Summary

Problems solved by technology

In this type of technology, on the one hand, user ratings are subjective and the data is noisy; on the other hand, the information implied by the matrix relationship is less, resulting in inaccurate recommendation results.

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