Commodity recommendation method and system based on deep neural network

Abstract

The invention belongs to the technical field of intelligent recommendation, and discloses a commodity recommendation method and system based on a deep neural network, and the method comprises the steps: firstly enabling an input layer to obtain the features of a user and a commodity; enabling the embedding layer to perform user and commodity feature initial processing; secondly, enabling the usercommodity interaction layer to perform user feature and commodity feature interaction; performing potential feature depth extraction on the residual network layer; and finally, enabling the output layer to predict commodity recommendation required by the user through a sigmoid activation function. According to the method, non-related commodity recommendation of the platform to the user can be effectively reduced, and a deep residual network is used for replacing a common neural network in a neural collaborative filtering recommendation algorithm, so that high-order nonlinear features in the user article relationship data are captured; the problem of insufficient extraction of high-order nonlinear features due to the fact that a neural network used in an existing recommendation algorithm isrelatively simple is solved, so that a relatively good recommendation effect is achieved.

Description

technical field [0001] The invention belongs to the technical field of intelligent recommendation, and in particular relates to a product recommendation method and system based on a deep neural network. Background technique [0002] At present, with the continuous expansion of the scale of e-commerce, the number and types of commodities increase rapidly, and it takes a lot of time for customers to find the commodities they want to buy. This process of browsing a large amount of irrelevant information and products will undoubtedly cause consumers who are submerged in the problem of information overload to continue to lose. Many platforms have their own recommendation systems, which can analyze and recommend products related to their own platforms based on the user’s usual behavior habits and browsing records. However, the user’s usual accidental operations are also recorded, and the business platform often recommends some products that they are not interested in. Products or...

AI Technical Summary

Problems solved by technology

[0004] Although both NCF and ONCF use relatively simple neural networks to learn user-item interaction features, in the face of complex and highly sparse feature maps, this is not enough to combine complex and high-order user-item nonlinear features. I have learned deeply and cannot solve the existing problems of recommendation algorithms in the market

[0005] Through the above analysis, the problems and defects of the existing technology are: the existing simple neural network has the problem of gradient disappearance and gradient explosion, so that the extraction of high-order nonlinear features is insufficient, resulting in poor recommendation effect and many non-related product recommendations

[0006] The difficulty of solving the above problems and defects is: Although the traditional corresponding solution is data initialization and regularization, although this solves the problem of gradient and deepens the depth, it brings another problem, which is the degradation of network performance The problem, the depth deepened, but the error rate increased

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