Personalized recommendation cold start method and device and storage medium
A cold start and task technology, applied in the field of data processing, can solve the problems of low accuracy of personalized recommendation cold start, inability to obtain accurate and reliable recommendation models, etc., to achieve the effect of strong practicability, high practical value, and simple structure
Pending Publication Date: 2022-03-04
CHINA CONSTRUCTION BANK
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For business scenarios with less sample data, the cold start accuracy of personalized recommendation is low due to the inability to obtain accurate and reliable recommendation models
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[0077] The following will clearly and completely describe the technical solutions in the embodiments of the application with reference to the drawings in the embodiments of the application. Apparently, the described embodiments are only some of the embodiments of the application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.
[0078] The terms "first", "second", "third" and "fourth" (if any) in the specification and claims of this application and the above drawings are used to distinguish similar objects, and do not necessarily use Used to describe a specific sequence or sequence. It is to be understood that the data so used are interchangeable under appropriate circumstances such that the embodiments of the application described herein can be practiced in sequences other than those illustrated or describ...
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The invention provides a personalized recommendation cold start method and device and a storage medium, and the method employs a small sample learning thought to solve the problem of recommendation cold start of a first personalized recommendation task under the condition of less samples, i.e., a first recommendation model is obtained through training of a second personalized recommendation task with more sample data, and the recommendation cold start is more personalized. And the sample data of the first personalized recommendation task is utilized to adjust the first recommendation model to obtain an accurate and reliable recommendation model, so that the personalized recommendation cold start accuracy is improved. Moreover, the problem of cross-scene use of user behaviors is solved by utilizing a meta-learning technology, so that the user can be migrated and applied to another subject matter with sparse behaviors under the condition that the user has more interested behaviors on the subject matter of one type. In addition, the recommendation model used in the application is simple in structure and high in feasibility, and has a relatively high practical value.
Description
technical field [0001] The present application relates to data processing technology, and in particular to a personalized recommendation cold start method, device and storage medium. Background technique [0002] With the popularity of various applications, enterprises can collect more and more complete user data. How to use these data to increase revenue is a problem that all enterprises will face. The most common way is personalized recommendation. The main goal of personalized recommendation is to recommend a large number of subject matter to a large number of users who may like it. For example, in the financial field, recommend items of interest to users, such as funds, wealth management, insurance, etc. [0003] With the development of science and technology, personalized recommendation has been applied in many fields. Personalized recommendation aims to estimate the possibility of interaction between node information based on the interaction history, for example, based...
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IPC IPC(8): G06F16/9535G06K9/62
CPCG06F16/9535G06F18/214
Inventor 许娜娜
Owner CHINA CONSTRUCTION BANK