A method, device and medium for real-time clustering recommendation
A clustering algorithm and clustering technology, applied in other database clustering/classification, buying/lease transactions, instruments, etc., can solve the problem that the recommendation results are difficult to satisfy, so as to improve the user experience, ensure accuracy, and avoid recommendation effect of error
Active Publication Date: 2022-03-22
天元大数据信用管理有限公司
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In the existing technology, it is difficult for the recommendation system to update the user's recommendation results every day to meet the real-time update requirements of today's recommendation system
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[0021] In order to make the purpose, technical solution and advantages of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with specific embodiments of the present application and corresponding drawings. Apparently, the described embodiments are only some of the embodiments of the present application, rather than all the embodiments. 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.
[0022] A real-time clustering recommendation method described in the embodiment of the present application is stored in a corresponding system or server, and the user can log in through a terminal to enter the system or server, thereby realizing the control of each parameter in the real-time clustering recommendation method Adjustment, the terminal ca...
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The present application discloses a method, device and medium for real-time clustering recommendation. The method includes: acquiring multiple users who have multiple user characteristics; performing initial clustering on user characteristics by a clustering algorithm to obtain the first clustering result Carry out accuracy evaluation on the first clustering result by the evaluation algorithm, for each type in the first clustering result, if it meets the accuracy requirement, it will be passed, if it does not meet the accuracy requirement, adjust the parameters of the clustering algorithm, and Carry out re-clustering until all the accuracy requirements are met to obtain the second clustering result; for each category in the second clustering result, recall it from the commodity library in real time to obtain multiple commodities corresponding to each category, and according to multiple Items are recommended to users included in this class. It can ensure the accuracy of user clustering. At the same time, it improves the user experience, ensures the running speed of the system architecture, and avoids problems such as delays and freezes caused by occupying too many server resources.
Description
technical field [0001] The present application relates to the field of real-time clustering recommendation, in particular to a method, device and medium for real-time clustering recommendation. Background technique [0002] In the field of product recommendation based on user characteristics, collaborative filtering is the most extensive and successful recommendation technology. However, with the increase in the number of users and the number of products in e-commerce systems, it is difficult to search for the "nearest neighbor" users corresponding to the target users in all user groups. The time consumption is also increasing sharply, resulting in a decline in the performance of the recommendation system. [0003] At the same time, the user's behavior is constantly changing, and it is the development trend of the recommendation system to make targeted recommendations based on the user's real-time changing behavior. In the existing technology, it is difficult for the recomm...
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IPC IPC(8): G06Q30/06G06F16/9536G06F16/906
Inventor 任德鑫崔乐乐边松华
Owner 天元大数据信用管理有限公司