Optimal training method of collaborative filtering recommendation model

Abstract

The invention discloses an optimal training method of a collaborative filtering recommendation model, belonging to the technical field of data mining and individual recommendation. The optimal training method comprises the following steps of: arranging hidden characteristic matrixes seperately, so as to eliminate the interdependency of user hidden characteristics and item hidden characteristics in a training process; secondly, dividing the hidden characteristic matrixes into a user hidden characteristic training process and an item hidden characteristic training process which separate stochastic gradient descent; and finally, executing the user hidden characteristic training process and the item hidden characteristic training process. With the adoption of the optimal training method provided by the invention, the interdependency of the user hidden characteristic matrixes and the item hidden characteristic matrixes can be eliminated by optimizing a training process of the collaborative filtering recommendation model, so that the expandability is improved; the rate of convergence is fast; the training round number desired by the convergence is less; and the establishment speed of the recommendation model can be improved.

Description

technical field [0001] The invention belongs to the technical field of data mining and personalized recommendation, and in particular relates to an optimization training method of a collaborative filtering recommendation model. Background technique [0002] The explosive growth of Internet information scale has brought about the problem of information overload. Excessive information is presented at the same time, making it difficult for users to filter out the parts that are effective for individuals, and the information utilization rate is reduced instead. Personalized recommendation technology is an important branch in the field of data mining research. The goal is to provide intelligent services of "finding people with information" by establishing a personalized recommendation system, so as to fundamentally solve information overload. [0003] As a source of recommendation, the recommendation model is the core component of a personalized recommendation system, and the rec...

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Problems solved by technology

[0003] As a source of recommendation, the recommendation model is the core component of a personalized recommendation system, and the recommendation model based on matrix factorization is a widely used recommendation model because of its good recommendation accuracy and scalability. At present, Personalized recommendation technology can analyze the internal relationship between users and information based on the user's historical behavior, so as to connect users with the information they may be interested in, and provide intelligent services for information to find people, thereby solving the problem of information overload. Recommended As the core component of the recommendation system, the model is the focus of research in the field of personalized recommendation technology. Because the matrix factorization recommendation model has high recommendation accuracy and good scalability, it has a wide range of applications. However, due to During the training process of the matrix factorization recommendation model, its user latent features and item latent features are interdependent and cannot be parallelized, thus limiting the further promotion of the model

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