Personalized service recommendation system and method

Abstract

The invention discloses an individualized service recommendation system and a corresponding method including the following steps: various operation information existing on a terminal is monitored by a user information collector and after a pre-processing the operation information is stored in a user information database; and if the user information database is updated, a user behavior analyzer is started for analysis; the user behavior analyzer scans the user information database and extracts the new user information and stores the new information in a resource information database, and then new recommendation strategies are computed and stored in a recommendation strategy database; a context sensing processor senses the present context of the user and sends out the present context description information and an individualized recommendation processor is started; the individualized recommendation processor acquires the present context information after receiving the information from the context sensing processor and also acquires a matched optimal recommendation strategy by searching the recommendation strategy database and then matches the proper resource information according to the optimal recommendation strategy and by searching the resource information database so as to generate the individualized recommendation service in real time.

Description

technical field [0001] The invention relates to the technical field of computer applications, in particular to a personalized service recommendation system and method. Background technique [0002] The personalized recommendation system is used to help users find interesting content in a large amount of information. The "personalized" service it embodies is now more and more accepted by many fields such as business websites and electronic libraries, and has become one of them. important function. Personalized service technology provides different services for different users to meet different needs. Personalized services learn users' interests and behaviors by collecting and analyzing user information, so as to achieve the purpose of active recommendation. There are many personalized service systems at present, and they put forward various ideas to realize personalized service. According to the recommendation technology adopted, it can be divided into two types: rule-base...

AI Technical Summary

Problems solved by technology

The disadvantage is that user profiles cannot be shared between different web applications

[0011] The advantage of storing user description files on the agent is that it can not only support content-based filtering and collaborative filtering, but also support the sharing of user description files among different web applications. The disadvantage is that user description files may need to be transmitted

[0012] The advantage of storing user description files on the client is that user description files can be shared between different applications, but the disadvantage is that only content-based filtering can be performed, which limits the ability to recommend

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