Service recommendation method based on user risk preferences

Abstract

The invention relates to a service recommendation method based on user risk preferences. According to the service recommendation method based on the user risk preferences, an attribute extracting method based on the user risk preferences and the service recommendation method based on the user risk preferences are used. The attribute extracting method based on the user risk preferences is a simplifying method according to the characteristic of multiple attributes of services. According to the service recommendation method based on the user risk preference, considering that different users have different inclinations to take risks of unknown services, the users are classified according to the different user risk preferences, corresponding rules are adopted for different types of users, effective attributes better meeting requirements of the different types of users are extracted from multiple non-functional attributes from the services, similarity calculation is conducted, and a recommendation is formed finally. By the adoption of the service recommendation method based on the user risk preferences, simplification of the multiple attributes of services is achieved, a solution to the problem that uncertain intervals exists when the non-functional attributes of the services are grated during service recommendation is provided, in this way, recommendation results can better meet the requirements of the different types of users, and a powerful method and tool are provided for service recommendation.

Description

technical field [0001] The present invention is a scheme for implementing classification and recommendation of services according to different needs of users under an open and heterogeneous complex network environment. Aiming at the characteristics of users' complexity and different expectations for unknown services in service recommendation, a service recommendation method based on user risk preference is proposed, which is suitable for service recommendation. Features, integrate the user's risk preference into the field of service attribute reduction and recommendation, so that the recommendation result is more in line with the user's expectations. It belongs to the field of information service and service computing. Background technique [0002] Service selection means that the user selects the required service from candidate services with the same functional attributes but different non-functional attributes in a certain way according to his own needs, so as to execute ...

AI Technical Summary

Problems solved by technology

Service recommendation is different from traditional recommendation methods. Users have clear application requirements, but it is difficult to judge the extent to which the service meets their own preferences when faced with a large number of services with the same function.

[0003] With the development of information technology and the expansion of the amount of information, most decision makers face too much information instead of too little when making decisions, so how to use existing technology to filter massive information to extract effective information and use effective information Making decisions has become an important research content in the field of decision-making. The reduction of a large number of service non-functional attributes in the field of service recommendation is a necessary process. The understanding of effective service attributes is different, and the impact of unknown evaluation on user needs is ignored. When users face a large number of unknown information choices, it means taking certain risks, and users have different expectations for unknown evaluation. If recommended Failure to take this into account by the system will create serious deviations from user expectations

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