Product recommendation method and device

Abstract

The embodiment of the invention provides a product recommendation method and device. The method is used for determining whether to recommend a to-be-recommended product to a target user. The method comprises the steps that multi-field information associated with the target user is acquired, wherein the information comprises purchase data of the target user in the product field of the to-be-recommended product and purchase data in other product fields; a user feature matrix of the target user is constructed according to the multi-field information; for one to-be-recommended product, a user feature matrix of multiple users who purchase the to-be-recommended product is acquired, and a product feature matrix of the to-be-recommended product is obtained based on feature values in the matrix; the user feature matrix and the product feature matrix are input into a machine learning model to obtain user preference vectors and product preference vectors; a selection assessment value between theto-be-recommended product and the target user is obtained according to the user preference vectors and the product preference vectors; and when the selection assessment value is greater than a predetermined recommendation threshold, it is determined that the to-be-recommended product is recommended to the target user.

Description

technical field [0001] The present disclosure relates to the technical field of data processing, and in particular to a product recommendation method and device. Background technique [0002] In the field of product recommendation, cold starts and data sparsity are common problems. Cold start refers to product recommendation without a large amount of user data support; data sparseness means that the items that interact with users are only the tip of the iceberg of the overall items, resulting in extremely sparse data in the user-item scoring matrix. For example, in the recommendation of financial wealth management products, due to the nature of the financial wealth management industry itself, such as large transaction volume and low frequency, user behavior information is scarce, and there is not a large amount of user data for product recommendation, resulting in cold start problems; Moreover, users' purchases of financial wealth management products only account for a smal...

AI Technical Summary

Problems solved by technology

For example, in the recommendation of financial wealth management products, due to the nature of the financial wealth management industry itself, such as large transaction volume and low frequency, user behavior information is scarce, and there is not a large amount of user data for product recommendation, resulting in cold start problems; Moreover, the purchase behavior of users’ financial wealth management products only accounts for a small part of the total registered users of the wealth management product platform, and the problem of data sparseness is also very prominent

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