Personalized recommendation system and method

Abstract

The invention relates to the technical field of recommendation systems based on mass data and data mining, in particular to a personalized recommendation system and method. The system comprises a data interface layer, a user log system, a knowledge base, an entity relation gallery and a recommendation calculation system. The data interface layer is used for being in communication with an upper layer service system. The user log system includes all operation records of a user in an application system. The knowledge base is a set of all data in the application system and a learning set of the recommendation system. The entity relation gallery is used for storing the incidence relation between the user, data entities, properties and the like. The recommendation calculation system automatically recommends topic data which the user is interested in to the user by integrating the preference of the user and the weight of the user and according to a specific algorithm. By means of the personalized recommendation system and method, the problem of the cold start of the recommendation system and the problem that when interest of the user changes ceaselessly, the recommendation calculation complexity is increased are solved; the personalized recommendation system and method can be used for processing mass data.

Description

technical field [0001] The invention relates to the technical field of recommendation systems based on big data and data mining, in particular to a personalized recommendation system and a recommendation method based on user logs and entity association gallery. Background technique [0002] In recent years, with the increasing development of information technology and the increasing number of users connected to the Internet, a huge amount of bit data is generated in the Internet all the time. How to effectively use these information has become a worldwide concern. After years of research and application in academia and industry, the technologies for mining and utilizing the added value of these data have gradually matured, most notably in e-commerce, online information applications (such as online news, online music, online film and television), etc. Domain related information recommendation system. [0003] The most widely used recommendation system is collaborative filte...

AI Technical Summary

Problems solved by technology

[0004] However, when the current mainstream recommendation system faces new users, the "cold start" problem caused by the lack of historical data as the basis for recommendation also makes the user experience relatively poor, and the user's data reading interest or preference is also constantly changing over time. , how to better solve the cold start problem of the recommendation system and adjust the recommendation results according to the user's preference changes at any time is also a hot topic of research and application.

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