Intelligent decision-making and entity recommending union system based on internet and work flow

Abstract

The invention provides an intelligent decision-making and entity recommending union system based on an internet and work flow. The union system consists of a rapid application and development platform, a client end and a decision-making and recommending application system, wherein the decision-making and recommending application system comprises a portal website, a decision-making service development sub-system, an entity information distribution sub-system and a decision-making service usage sub-system. According to the union system, a developer utilizes the decision-making service development sub-system to carry out the development of a decision-making service example, a promulgator carries out the distribution of entity information by the adoption of the entity information distribution sub-system, a decision maker implements auxiliary decision making by utilizing the decision-making service usage sub-system, and obtains the information of recommended specific entity while obtaining a decision-making result. According to the union system, model establishment based on decision-making flow of work flow and entity recommendation technology based on body matching are realized, the decision-making service development sub-system and the entity information distribution sub-system have the characteristics of a cloud platform i.e. service, and the decision-making service example for the use of the decision maker has the characteristics of cloud software i.e. service.

Description

technical field [0001] The invention relates to a network-based decision support system and commodity recommendation system, in particular to an intelligent decision-making and entity recommendation system based on the Internet and workflow. Background technique [0002] The traditional decision support system (Decision Support System, DSS) is a system that comprehensively utilizes a large amount of data, organically combines many models, and assists decision-making users at all levels to achieve scientific decision-making through human-computer interaction. DSS provides an environment for decision-making users to analyze problems, build models, and simulate decision-making processes and programs, and calls various information resources and analysis tools to help decision-making users improve their decision-making level and quality. Intelligent DSS is a DSS combined with an artificial intelligence expert system, which can deal with both quantitative and qualitative issues, a...

AI Technical Summary

Problems solved by technology

At present, there is no recommendation system that can simultaneously consider the characteristics of commodities (products, services and solutions), consumer needs and business needs to make recommendations. The reason for this phenomenon is that users' interests have not been described in the network environment. The effective mechanisms and models of preferences and their dynamic changes, therefore, fully personalized recommendation modeling theory and techniques still need further research

Liu Pingfeng et al., in the work of knowledge-based e-commerce intelligent recommendation system platform design [Computer Engineering and Application 2007, 43(19): 199-201, 216], using rules formulated in specific fields for rule-based and instance-based reasoning , and generate recommendations based on reasoning results. The acquisition and inference of rules, the construction of rule knowledge base, etc. are the key points of knowledge-based recommendation technology, but there is no theory and specific method for systematic intelligent recommendation.

[0006] The inventor believes that the application of the current personalized recommendation system is that the enterprise actively recommends to the user the products that the user may be interested in according to the user's hobbies and consumption behaviors, which is a passive recommendation for the user; while the intelligent DSS Use is the process in which decision-making users actively make auxiliary decisions to select target instances in the solution. However, it is necessary to establish an intelligent DSS in advance according to the needs of decision-making users and collect a large number of target instances to choose from. This is passive for enterprises. of

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