Mobile e-commerce recommendation method and system based on multi-source information fusion

Abstract

The invention discloses a mobile e-commerce recommendation method based on multi-source information fusion, and the method comprises the steps: S1, obtaining user information and corresponding consumption data from a plurality of information sources, carrying out the preprocessing of the obtained user information and corresponding consumption data, obtaining original evidence bodies, and enablingthe original evidence bodies to be divided into a plurality of types; S2, based on the original evidence body, calculating evidence weight of each type of data in the original evidence body through aradial basis function and a neural network algorithm, the evidence weight being used for distinguishing recommendation values of each type of data; S3, according to the evidence weight, applying D-S evidence theory to carry out information fusion on the original evidence body to obtain a new evidence body; and S4, processing the new evidence body by adopting a power spectrum estimation method to obtain a recommendation decision. According to the method, the recommendation accuracy, the recommendation indirectly and the coverage rate can be effectively improved, and the recommendation service performance is improved.

Description

technical field [0001] The present invention relates to the technical field of information fusion, in particular to a mobile e-commerce recommendation method and system based on multi-source information fusion. Background technique [0002] With the change of user consumption habits, mobile e-commerce has become a trend. However, continuously generating large amounts of data is not only inconvenient for consumers when searching for meaningful products, but also means that some products are rarely purchased. The lack of in-depth mining of user and resource information has become the main bottleneck restricting the predictive analysis of mobile commerce recommendation systems. [0003] In order to solve the above problems, the recommendation system was invented and widely used by e-commerce practitioners. It has become an important research topic in information science and decision support systems. Currently, research on recommender systems generally includes content-based f...

AI Technical Summary

Problems solved by technology

However, there are few studies on multi-source information fusion based on location in mobile e-commerce recommender systems

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