Product recommendation method, device, equipment and storage medium

Abstract

The invention relates to data analysis, and particularly discloses a product recommendation method, a device, equipment and a storage medium. The method comprises the steps of when an access request of a first user is received, acquiring the historical product information corresponding to the first user; based on the historical product information, determining feature similarities and historical product similarities of a plurality of different first users, the historical product information being stored in a block chain; determining a candidate product information set corresponding to the candidate target user based on the feature similarity and the historical product similarity; and when the product information corresponding to the access record corresponding to the candidate target userexists in the candidate product information set, calculating the interestingness of the candidate target user for each candidate product, and pushing according to the interestingness. When a product is recommended to a user, the cold start problem of common collaborative filtering is effectively relieved.

Description

technical field [0001] The present application relates to the technical field of data analysis, and in particular to a product recommendation method, device, equipment 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., all of which use historical data as a reference. [0004] However, when users inquire about insurance information online, the insurance recommendation platform usually recommends the types of insurance that many users have purchased, or uses the user's own historical dat...

AI Technical Summary

Problems solved by technology

[0004] However, when users inquire about insurance information online, the insurance recommendation platform usually recommends the types of insurance that many users have purchased, or uses the user's own historical data as a reference. However, if one kind of insurance is used to recommend another target insurance, it involves the problem of cold start of product recommendation. Therefore, it is necessary to propose a method for accurately recommending products for current users.

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