Electronic commerce recommendation mobile crowd system based on cooperative data collection and embedded control
© Wang et al. 2016
Received: 15 December 2015
Accepted: 29 January 2016
Published: 24 February 2016
It is known that the collection of the specific needs of mobile users and location management in an electronic commerce recommendation system are important indicators used to evaluate user satisfaction and system execution efficiency. In order to improve the low accuracy of recommendation systems and ensure real-time location management, we proposed cooperative embedded data collection and control of the electronic commerce recommendation system for mobile crowd system. First of all, we would assign every user with a mobile crowd terminal. The terminal could collect the user’s demand and personalized data. An embedded terminal could provide users with a real-time, accurate, personalized shopping experience. The terminal could collect the user demand dynamic evolution function. Then, the electronic commerce recommendation application platform would be formed through the deployment of a mobile crowd terminal. The platform collects and forwards the signal acquisition through a single port, dual port and port more adaptive network structure. Experimental results show that the performance of the proposed scheme is superior to the electronic commerce recommendation system based on user personalization drive, such as the data collection of the embedded crowd terminal delay, precision and accuracy of electronic commerce recommendation, as well as the number of iterations. The proposed scheme is more suitable for an electronic commerce recommendation system.
With the development of mobile Internet and electronic commerce (e-commerce), trading volumes soared. The structure of the e-commerce system is complex  and providing users with a large amount of commodity information can lead to a bottleneck. E-commerce recommendation system  can communication with users and percept the user demand for providing powerful purchase guarantee. However, e-commerce recommendation system has the poor ability of the user demand perception, data collection and system management.
The mobile crowd system has been used by researchers to provide data support for e-commerce recommendation. Zhao et al.  presented the three-layer framework for mobile data collection in wireless sensor networks, which includes the sensor layer, cluster head layer, and mobile collector layer. Ye et al.  proposed a context-aware model combined with a crowd-sensing paradigm to achieve fine-grained measurement of a user’s current context.
Here, our key issue is the improvement of the data collection of user e-commerce requirements. Velmani and Kaarthick  proposed the Velocity Energy-efficient and Link-aware Cluster-Tree (VELCT) scheme for data collection in wireless sensor networks (WSNs), which would effectively mitigate the problems of coverage distance, mobility, delay, traffic, tree intensity, and end-to-end connection. An Asynchronous Distributed Data Collection algorithm with fairness consideration for cognitive radio networks (CRNs) was proposed by Zhipeng et al. . Simplicio et al.  proposed the SecourHealth, which is a lightweight security framework focused on highly sensitive data collection applications. Zhaosheng et al.  proposed the multi-threshold control repair method to clean and repair the probe vehicle data. Lu et al.  used game theoretic analysis based on coding-aware peer-to-peer data repair in multi-rate wireless networks.
The rest of the paper is organized as follows. Section 2 describes the cooperative data collection mechanism in the e-commerce system. In Section 3, we describe the mobile crowd system for e-commerce recommendation with embedded control. Simulation results are given in Section 4. Finally, we conclude the paper in Section 5.
2 Cooperative data collection mechanism in the e-commerce system
To provide a user with a real-time accurate e-commerce recommendation system and personalized shopping experience, its dynamic evolutionary process requires collection of the user’s specific needs and mobility management and configuration for each user with a user’s demand and personalized data collection of mobile terminal and its application.
In Equation (1), NMC represents mobile group of terminal size, N represents user scale, DSC represents user-generated data size, dc represents mobile group of terminals and data exchange center distance, Ce represents the server group of computing power and recommend computing center, and the space gain of SG represents computing center.
In Equation (2), the DTH is the collecting data threshold of mobile terminal.
State detection. If the number of 1 STMC in the binary representation is greater than 2 in STMC, there is an error.
Transfer can be divided into passive and active.
To transfer to the target state.
If the target state is unable to be implemented, the best passive transfer target state would be selected through cooperative data collection mechanism.
Initiative to transfer factor including data size and active mobile terminal size.
When mobile crowd terminal is in a state of “transfer”, the mobile crowd system can actively enter the active state.
Transfers of active, dormant and idle states have linear one-way characteristics.
Only idle mobile crowd system can enter a state of transfer and join the cooperative data collection.
3 Mobile crowd system for e-commerce recommendation with embedded control
How to design a mobile embedded terminal which can collect the user’s personalized needs.
How to realize the service launch and accept the e-commerce recommendation.
How to monitor the related factors that affect e-commerce recommendation service. Based on the scheme, the system has the function of intelligent control and restoration.
In Equation (4), k represents mobile group of intellectual equipment port number, alpha predictor represents user requirements, and N represents user scale.
4 Performance analysis and verification
The embedded intelligent terminal data collection delay.
The way time needed for data collection through collaboration between the embedded intelligent terminals, including collaboration delay overhead and data fusion computational overhead.
Data collection of the embedded intelligent terminal data precision.
Forward embedded intelligent terminal to the e-commerce system of data and user experience data contrast.
The e-commerce recommendation accuracy.
E-commerce recommendation system would make the users ordering scheme for satisfying the actual user requirements.
The e-commerce recommended number of iterations.
It is the recommend iteration number of completing e-commerce recommendation user requirements.
The participants of our experiment were undergraduate and graduate students of Nanjing University of Posts and Telecommunications. We obtained our experimental data of the personalized e-commerce recommendation conclusion through a network investigation and questionnaire survey. At the same time, personalized user demand data from 10 professionals from the liberal arts, sciences and engineering were gathered for comparison.
Because the mobile embedded terminal of the ECR - CD scheme could obtain the user personalized e-commerce recommendation requirements. At the same time the ECR - CD scheme realized the real time launch and high precision reception of the e-commerce recommendation service. In addition, the scheme can intelligently monitor the related factors that affect the e-commerce recommendation service, and so has real-time intelligent control and the e-commerce recommendation data repair function has high reliability. Thus the high performance are shown in Fig. 8 and Fig. 9. The ECR - CD scheme not only keep the high recommendation precision of e-commerce but also reduce the iterations number.
In order to improve the recommend accuracy and real time location management of the e-commerce recommendation system, the mobile crowd e-commerce recommendation system was proposed based on the cooperative embedded data collection and control. The mobile crowd terminal of each user in this system could collect the user demand and the personalized data, which is used to provide accurate real-time user personalized shopping experience and dynamic evolving requirements. There are the collecting and forwarding signal network structure with a single port, dual port and multiple port in the terminal. The experimental results show that the performance of the proposed ECR - CD scheme has obvious advantage, such as the embedded intelligent terminal delay, precision and accuracy of e-commerce recommendation and the iterations number.
This work is supported in part by the national college students’ innovative training program (SZDG2015031), University philosophy social science research project in Jiangsu province education department (2015SJB014), and the fund incubator project of Nanjing University of posts and telecommunications countries (NY214117).
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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