Article recommendation method based on generalized nerve attention

Abstract

The invention provides an article recommendation method based on generalized nerve attention, and relates to the technical field of information processing. According to the method, a generalized matrix factorization model GMF and a nerve attention similarity model NAIS are combined to establish a generalized nerve attention recommendation model GNAS; the model is optimized by using an attention mechanism integrated by GMF and an MLP (Multilayer Perceptron) in the model; and after the model is optimized, the preference degree of the user to the target object is predicted through the optimized generalized neural attention recommendation model, and a personalized recommendation list is generated for the user. Potential hobbies and interests of users are mined, and interpretability and diversity of a recommendation system are improved; secondly, an attention mechanism combining a GMF model and an MLP model is adopted to estimate the weight of each historical article when the favorite degree of the target article is predicted, the recommendation accuracy is greatly improved with low time cost, and articles more conforming to the interest of the user are recommended to the user.

Description

technical field [0001] The invention relates to the technical field of information processing, in particular to an item recommendation method based on generalized neural attention. Background technique [0002] Today, we are experiencing the transition from the Information Technology (IT) to the Data Technology (DT). The more obvious sign of the Data Age is information overload. How to quickly help specific users find the information they are interested in from the massive amount of information? Now there are two related solutions: search engines and recommendation systems. Search engines require users to accurately describe their needs, while recommendation systems discover users' individual needs and interests by analyzing and mining user behavior, and recommend information or items that users may be interested in to users. An excellent recommendation system can connect users, merchants, and platform parties well, and benefit all three parties. Therefore, it has not only...

AI Technical Summary

Problems solved by technology

However, user interests are changing all the time. Due to the deep depth of the neural model and the complexity of the model, the single neural model has a strong generalization ability, but the most original interaction information between users and items is ignored. , the model lacks memory ability, and some recommended items may deviate from the user's interest

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