Personalized recommendation method based on variable precision tolerance relation rough set expansion

Abstract

The invention provides a rough set expansion personalized recommendation method based on the variable precision tolerance relation. By means of two object indistinguishability characteristic analysis which is more consistent with the statistical significance, a core set is acquired by means of the discernable matrix, and discernable matrix elements containing core attributes are emptied. A reduction set is initialized into the core set, corresponded reduction and union are performed to attributes appearing mostly of discernable matrix non-empty element with those of the reduction set, fresh reduction is acquired, the elements with the attributes in the discernable matrix are emptied, and extraction of the reduction set is implemented. Focusing on each reduction, a knowledge tree is established, pruning conditions are determined, special pruning can be performed according to the pruning conditions, calculation complexity is reduced, effective rules are acquired, a rule set is formed, and data excavation is implemented effectively. According to the condition attribute value of the result to be recommended, rule match is performed in the rule set, and personalized recommendation can be implemented.

Description

Technical field [0001] The invention belongs to the technical field of big data processing of decision information systems, especially the processing and personalized recommendation of incomplete decision information systems, and specifically designs a personality based on variable-precision tolerance relation extended rough set (Variable-precision Tolerance relation, VT) Recommended method. Background technique [0002] In recent years, with the development of web applications such as portals, social networks, and e-commerce, a large amount of data has been generated and accumulated. These data have the characteristics of Volume (large), Velocity (high speed), Variety (diversity), Value (value), etc. . But at the same time, these big data are often not complete, that is, it is difficult to meet the requirement that all relevant attribute values ​​are determined. For example, in terms of e-commerce understanding of users, from the perspective of shopping habits alone, you can s...

AI Technical Summary

Problems solved by technology

[0011] The purpose of the present invention is to solve the problem that there are more and more incomplete decision-making information systems today, and the application prospect of personalized recommendation is getting wider and wider. The advantages of extended rough sets for incomplete decision information processing, by establishing a variable precision tolerance relationship that is more in line with statistical significance, define a new extended rough set, and build a new resolution matrix on this basis, from reducing the calculation From the perspective of complexity, obtain reduced sets, mine effective decision rules, and achieve more reasonable personalized recommendations

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