Hyperspherical collaborative metric recommendation device and method based on pre-trained semantic model

Abstract

The invention discloses a hyperspherical collaborative metric recommendation device and method based on a pre-training semantic model, the device comprises a pre-training latent semantic module and acollaborative metric recommendation module, the pre-training latent semantic module comprises an article text information encoder and a decoder, and the collaborative metric recommendation module comprises a hyperspherical mapping module and a fusion loss function module; text information vectorization representation of the article is obtained by an encoder and a decoder; the hyperspherical surface mapping module maps the initialized positive and negative users and the article implicit vectors into the same high-dimensional hyperspherical surface manifold in an angle measurement mode, and thefusion loss function module trains the user and article implicit vectors after hyperspherical surface mapping and optimizes the intra-class and inter-class distances of the positive and negative userarticle sample pairs; and during prediction, a text vector of the article and a corresponding user article implicit vector are obtained while prediction is performed, and an article recommendation result of the user is obtained by calculating a cosine distance between the text vector and the corresponding user article implicit vector.

Description

technical field [0001] The present invention relates to computer applications, in particular to a hypersphere (or hyperdimensional sphere) collaborative measurement recommendation device and method based on a pre-trained semantic model. Background technique [0002] The recommendation algorithm belongs to the interdisciplinary subject of artificial intelligence and computer technology. The task of the recommendation algorithm is to analyze the user's historical behavior data and computer algorithm modeling to predict the user's favorite items and give recommendations. The advancement of recommendation system algorithms has improved people's efficiency and experience in processing massive amounts of network information in the face of "information overload". For content creators, an efficient recommendation system can help content spread on the platform faster, Accurately reach target users and improve the efficiency and quality of content dissemination. Commonly used shoppin...

AI Technical Summary

Problems solved by technology

Although many advanced methods have been proposed to improve the performance of recommendation models, the problem of cold start based on items still exists.

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