Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business
A lack of in-depth excavation of user and resources information has become the main bottleneck restricting the predictive analytics of recommendation systems in mobile commerce. This article provides a method which makes use of multi-source information to analyze consumers’ requirements for e-commerce recommendation systems. Combined with the characteristics of mobile e-commerce, this method employs an improved radial basis function (RBF) network in order to determine the weights of recommendations, and an improved Dempster–Shafer theory to fuse the multi-source information. Power-spectrum estimation is then used to handle the fusion results and allow decision-making. The experimental results illustrate that the traditional method is inferior to the proposed approach in terms of recommendation accuracy, simplicity, coverage rate and recall rate. These achievements can further improve recommendation systems, and promote the sustainable development of e-business.
Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business
A lack of in-depth excavation of user and resources information has become the main bottleneck restricting the predictive analytics of recommendation systems in mobile commerce. This article provides a method which makes use of multi-source information to analyze consumers’ requirements for e-commerce recommendation systems. Combined with the characteristics of mobile e-commerce, this method employs an improved radial basis function (RBF) network in order to determine the weights of recommendations, and an improved Dempster–Shafer theory to fuse the multi-source information. Power-spectrum estimation is then used to handle the fusion results and allow decision-making. The experimental results illustrate that the traditional method is inferior to the proposed approach in terms of recommendation accuracy, simplicity, coverage rate and recall rate. These achievements can further improve recommendation systems, and promote the sustainable development of e-business.
Mobile e-Commerce Recommendation System Based on Multi-Source Information Fusion for Sustainable e-Business
Yan Guo (Autor:in) / Chengxin Yin (Autor:in) / Mingfu Li (Autor:in) / Xiaoting Ren (Autor:in) / Ping Liu (Autor:in)
2018
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
Location Recommendation of Digital Signage Based on Multi-Source Information Fusion
| DOAJ | 2018
Multi-Source Information Fusion Based on Data Driven
| British Library Conference Proceedings | 2011
OCA: Ordered Clustering-Based Algorithm for E-Commerce Recommendation System
| DOAJ | 2023