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Backward learning-based dynamic multi-attribute service selecting method

A service selection, multi-attribute technology, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve the problem of increasing user difficulty, difficult to adapt to system function self-adaptation, pre-judgment, without considering service operation history information And other issues

Inactive Publication Date: 2011-07-27
GUILIN UNIV OF ELECTRONIC TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] These methods either rely too much on the objective non-functional attribute information of candidate services; or require users to give detailed user requirements when they put forward requirements, which increases the difficulty for users to use and makes users participate too much in the interaction of the system
Moreover, the existing service selection methods do not consider the historical information of service operation, and cannot reflect the predictive and guiding role of historical operation information on service selection.
Therefore, the existing service selection methods are difficult to meet the needs of "adaptive and pre-judgment" system functions in the surrounding intelligent environment

Method used

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  • Backward learning-based dynamic multi-attribute service selecting method
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  • Backward learning-based dynamic multi-attribute service selecting method

Examples

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Embodiment Construction

[0040] This example is to use this method in a home surrounding intelligent environment. An implementation system of this method is designed based on the Java platform and Jena2.6.2 in the home-surrounding intelligent environment. The ontology and reasoning rules adopt HOAO and HOAO-R. The system has the semantic matching reasoning ability of service functional attributes and non-functional attributes.

[0041] Ⅰ. User preference learning based on backward learning

[0042] In the surrounding intelligent environment of the home, multiple users use various services provided in the surrounding intelligent environment through personal mobile terminals, and learn the user's preference habit by invoking and evaluating a centralized service of a candidate service. The flowchart of user preference learning based on backward learning is as follows: figure 1 shown.

[0043] Ⅰ-i. Register multiple candidate services in the service registry as a learning set.

[0044] Construct user p...

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Abstract

The invention provides a backward learning-based dynamic multi-attribute service selecting method. The method comprises the following steps: 1, user preferential learning based on backward learning: initializing a service set, a user set, a service evaluation level set and a user non-functional attribute evaluation link (UQEL) table; calling service several times and giving evaluation by a user, mapping the user evaluation on service to evaluation on corresponding non-function attribute, adding to the UQEL table of the user to acquire a user preferential table finally; and 2, weight-based dynamic multi-attribute service selection: generating a candidate service set according to user requirement to acquire a user preferential set, calculating the weight of each non-functional attribute to generate a dynamic decision matrix sequence, calculating weight included angle cosine of a user preferential vector and a candidate non-functional attribute vector, the weight of each observation time, and the weight cosine sum of each candidate service in all observation times, and recommending the service with the maximum weight sum to the user. The method is used for realizing adaptive service selection without more participation of users, is convenient to use, and has good service selection adaptability.

Description

(1) Technical field [0001] The invention belongs to the field of surround intelligence and service computing, and relates to service selection decision-making technology, specifically an adaptive service that is suitable for surround intelligence environments, obtains user preference information based on backward learning, and realizes user needs through weighted dynamic multi-attribute decision-making. The selection method is a dynamic multi-attribute service selection method based on backward learning. (2) Background technology [0002] In the surrounding intelligent environment, users can get consistent service access and obtain services at any time and any place. The service here refers to the web service, that is, a self-describing and self-contained application module, which is remotely accessed and invoked through a standard protocol. A web service consists of functional properties and non-functional properties. Functional attributes indicate what the service can do...

Claims

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Application Information

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IPC IPC(8): G06F17/30
Inventor 张会兵张勇张敬伟刘连海钱俊彦王雪松
Owner GUILIN UNIV OF ELECTRONIC TECH
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