Product recommendation method and device and storage medium

Abstract

The embodiment of the invention provides a product recommendation method and device and a storage medium. In some exemplary embodiments of the present application, a meta-path set associated with therecommended service scene is selected from the insurance knowledge graph in combination with the recommended service scene, and for the target client, a similar client list of the target client and asimilar product list of the insurance products purchased by the target client are generated according to the entity characteristics contained in the plurality of meta-paths and the service relationship existing between the entity characteristics; collaborative filtering recommendation is performed according to the client similarity list and the similar product list to obtain a first product set capable of being recommended to the target client, a recommendation strength score of each product in the first product set, a second product set and a product recommendation strength score of each product in the second product set; according to the recommendation strength score of each product in the first product set and the product recommendation strength score of each product in the second product set, the target product recommended to the target user is determined. According to the product recommendation method, the insurance knowledge graph is utilized to fully express various semantic information, and the accuracy, the diversity and the interpretability of the product recommendation result are improved.

Description

technical field [0001] The present application relates to the technical field of data processing, and in particular to a product recommendation method, device and storage medium. Background technique [0002] With the development of the Internet and information technology, various business fields are becoming more and more electronic. For example, in the insurance business field, online insurance, online claims settlement, and online recommendation of insurance products are available. [0003] At present, commonly used algorithms for insurance product recommendation systems include collaborative filtering, matrix decomposition, and feature engineering-based logistic regression, decision trees, etc. These recommendation systems generally cannot take into account the three indicators of accuracy, diversity, and interpretability, and can only strike a balance between the three indicators. For example, to achieve diversity is generally at the cost of losing part of the accuracy...

AI Technical Summary

Problems solved by technology

These recommendation systems generally cannot take into account the three indicators of accuracy, diversity, and interpretability, and can only strike a balance between the three indicators

For example, to achieve di

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