Commodity recommendation method and commodity recommendation device, equipment and storage medium

Abstract

The invention provides a commodity recommendation method. The method comprises steps: a user information vector corresponding to associated user information of each user in a target user set and a commodity information vector corresponding to associated commodity information of each commodity in a target commodity set are extracted; the commodity information vector and the user information vectorare inputted to a corresponding depth neural network model trained and obtained according to user historical behavior data for feature extraction; according to the extracted commodity feature vector and a user feature vector, the correlation coefficient between each user and each commodity is calculated; and according to all correlation coefficients associated with each user, a commodity is recommended for each user. Correspondingly, the invention also corresponding provides a commodity recommendation device, computer equipment and a computer readable storage medium. Through the technical scheme of the invention, the cold start problem in the existing recommendation scheme can be effectively solved, the feature extraction difficulties are effectively reduced, and the commodity recommendation efficiency and the commodity recommendation effects are thus improved.

Description

technical field [0001] The present invention relates to the technical field of commodity recommendation, in particular, to a commodity recommendation method, a commodity recommendation device, computer equipment, and a computer-readable storage medium. Background technique [0002] At present, recommender systems and personalized recommendations are playing an increasingly important role in modern networks, and many network services are dedicated to helping users find the most relevant content in the shortest time. Generally, traditional recommendation systems can be divided into collaborative filtering-based recommendation systems and content-based recommendation systems. Collaborative filtering recommendation systems generally follow the principle of "similar users have the same hobbies and like the same things", and collaborative filtering recommendation can It is divided into user-based collaborative filtering recommendation, product-based collaborative filtering recomme...

AI Technical Summary

Problems solved by technology

[0005] Therefore, an object of the present invention is to propose a new product recommendation method, which can effectively solve the cold start problem in existing recommendation schemes, and support the recommendation of suitable products for new users, and directly use deep learning to User interests are modeled without the need for tedious manual feature extraction process, which effectively reduces the difficulty of feature extraction, thereby improving product recommendation efficiency and results

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