Method used by user to customize recommendation system in online system

Abstract

The present invention provides a method used by a user to customize a recommendation system in an online system, and belongs to the technical fields of data mining and network application. The method aims at a problem that an algorithm in a previous recommendation system is fixed and therefore a personalized recommendation system that meets demand of each user cannot be constructed. According to the method, a user customizes a personalized recommendation engine; a policy that a system algorithm self-adapts to a user is realized; moreover, relatively good recommendation diversity and relatively high recommendation accuracy are ensured; and the time period of a system in learning a user behavior is effectively shortened. The method comprises the following steps: step 1, according to a personal demand, a user starting setting of a recommendation engine in an online system; step 2, via a recommendation engine configuration page designed by the system, the user setting a recommendation engine parameter to customize a personalized recommendation algorithm that meets a personal preference of the user; and step 3, storing the configured recommendation engine and applying the configured recommendation engine in a recommendation system, so that according to the recommendation engine parameter set by the user, the system uses a combination policy to form a hybrid recommendation algorithm through combination, so as to calculate a recommendation list.

Description

technical field [0001] The invention belongs to the technical field of data mining and network application, and provides a method for user-customized recommendation system in an online system. Background technique [0002] With the increasing prosperity of the Internet economy, we are in an era of information explosion. How to screen out the information you are interested in from the massive amount of information has become a very difficult thing. However, with the implementation of big data ideas, the recommendation system has gradually received a lot of attention from the industry by virtue of its ability to help users find content they are interested in and recommend information and products that match their interests and hobbies. Therefore, various Internet industries have introduced recommendation technologies, such as e-commerce, movie and video websites, online music, social networking, etc., which have brought immeasurable benefits to the Internet economy. [0003] ...

AI Technical Summary

Problems solved by technology

[0006] In view of this, the purpose of the present invention is to provide a method for customizing a recommendation system for users in an online system. This method is aimed at the problem that the recommendation algorithm in the previous recommendation system is fixed, and it is impossible to construct a personalized recommendation system that meets the needs of different users. Using the method of user-customized personalized recommendation engine, the system can adaptively configure the recommendation algorithm according to the user's different preferences for personalized recommendation, and at the same time ensure that the recommendation results have better recommendation diversity and higher recommendation accuracy degree, effectively shortening the cycle for the system to learn user behavior

Images