A metapath context-based recommendation model training method and device

Abstract

The embodiment of the invention provides a metapath context-based recommendation model training method and device. The method comprises the following steps: acquiring a plurality of user information pairs in a sample information platform; for each user information pair, obtaining a path instance corresponding to the user information pair, and generating user characterization information, sample object characterization information and meta-path context characterization information corresponding to the user information pair; for each user information pair, splicing the above three kinds of information to obtain training samples; training the preset neural network model by using a plurality of training samples as inputs until the value of the function calculated by the loss function is smaller than the preset threshold value, and the neural network model is trained. Compared with the prior art, the scheme provided by the embodiment of the invention has the advantages that the trained recommendation model based on the metapath context can learn more optimized features, and furthermore, the accuracy of the recommendation result obtained based on the model can be improved.

Description

Technical field [0001] The present invention relates to the technical field of computer software, in particular to a method, device and electronic equipment for training a recommendation model based on meta-path context. Background technique [0002] At present, with the continuous development of network technology, more and more users choose to conduct various daily activities on the Internet, for example, buying commodities, watching movies, reading books, etc. With the development of network technology, the problem of information overload follows. Users feel the amount of information too large, which causes users to spend more time and energy to obtain the information they want. Take the purchase of goods as an example. With the continuous expansion of e-commerce, the types of goods are growing rapidly, and users need to spend a lot of time to find the goods they want to buy. And this process of needing to browse a large amount of irrelevant information to obtain the informat...

AI Technical Summary

Problems solved by technology

[0009] Therefore, since the influence of different meta-paths between the user and the object on the interaction between the user and the object is not considered in the prior art when training the recommendation model, the learned The features may not be optimal, and the accuracy of the recommended results obtained is low

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