Mobile E-Commerce Personalized Information Recommendation Model

Wang, Yue Ming; Wang, Rui Li; Xu, De Xun

doi:10.4028/www.scientific.net/amm.687-691.2136

Cited by 5 publications

(4 citation statements)

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“…This facilitates a discussion of mobile commerce's recommendation model that is both personalized and commercialized. This discussion provides insight into large data processing and cloud computing for mobile business [27].…”

Section: M-commerce Cloud and Privacy: A Literature Reviewmentioning

confidence: 99%

Community clouds within M-commerce: a privacy by design perspective

Shirazi

Iqbal

2017

J Cloud Comp

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The paper establishes the link between community clouds and m-commerce. The research questions attempt to provide an understanding of the various shopping domains and their convergence through mobile devices. In addition, insight is provided into the development and growth of mobile trends within the context of mcommerce and the cloud. This enables analysis of the Privacy by Design (PbD) framework and uses it to evaluate the next generation of m-commerce designs. This is followed by a discussion of the current issues based on the PbD framework, as well as offers of a solution framework to address the relevant challenges.

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Section: M-commerce Cloud and Privacy: A Literature Reviewmentioning

confidence: 99%

Community clouds within M-commerce: a privacy by design perspective

Shirazi

Iqbal

2017

J Cloud Comp

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“…The core task is to provide users with personalized product recommendation services by exploring the connection between users and products. Ultimately, achieving a win-win situation for both users and system owners [ 10 , 11 ]. Large-scale e-commerce recommendation systems in the context of modern Internet applications are facing challenges in the following aspects: The data processing capacity of recommendation systems with a centralized architecture is limited.…”

Section: Introductionmentioning

confidence: 99%

A Social-aware and Mobile Computing-based E-Commerce Product Recommendation System

Wang

2022

Computational Intelligence and Neuroscience

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E-commerce product recommendation system can help users to find their own products quickly from a large number of products. To address the shortcomings of the current e-commerce product recommendation system, such as low efficiency and large recommendation errors, we designed an intelligent recommendation system based on social awareness and mobile computing. The behavioral characteristics of the current e-commerce product recommendation system are analyzed; the e-commerce product recommendation system is built according to the data processing technology of mobile computing, and the key technologies of the e-commerce product recommendation system are designed. The test results show that the proposed system overcomes the shortcomings of the traditional e-commerce product recommendation system, speeds up the speed of users to find the products they really need from a large number of products, improves the accuracy of e-commerce product recommendations, and the error of e-commerce product recommendations is much lower than that of the traditional, which has higher practical application value.

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“…On social software, the platform will also recommend people you may know according to your personal information and contacts and recommend friends among users [21]. In business activities, the personalized recommendation system also has an important application value and carries out personalized recommendation of mobile business methods [22]. Between the system and users, a system simulation model of personalized information recommendation is established, the effectiveness of the model and the sensitivity of main influencing parameters are analyzed, and the characteristics and mechanism of information recommendation between the system and users are revealed [23].…”

Section: Introductionmentioning

confidence: 99%

Application and Analysis of Artificial Intelligence in College Students’ Career Planning and Employment and Entrepreneurship Information Recommendation

Zhang

Zheng²

2022

Security and Communication Networks

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With the arrival of graduation season, the number of graduates is expanding every year, and the employment rate of college students has become one of the data that colleges and universities pay attention to. To improve the employment rate of college students, we should first educate each college student in career planning, so that they can clearly understand their own positioning and future development direction. Career planning can guide students to understand their jobs and analyze their professional fields. Faced with massive employment information, it is also a big problem for college students to search and choose information. In this paper, the personalized recommendation system for entrepreneurship is described, and the basic principles of information recommendation are described. The basic information and personal interest points of college students are represented by feature vectors, which provide a favorable theoretical support for college students’ career planning and employment and entrepreneurship information recommendation. An information recommendation model under deep learning is formed. Finally, the performance of the model under the traditional algorithm and the optimized information recommendation model is evaluated, and the satisfaction of users to this system is scored so as to provide a convenient and quick information recommendation system for college students, thus indirectly improving the employment rate of graduates and providing corresponding solutions to the problem of difficult employment.

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Mobile E-Commerce Personalized Information Recommendation Model

Cited by 5 publications

References 0 publications

Community clouds within M-commerce: a privacy by design perspective

Community clouds within M-commerce: a privacy by design perspective

A Social-aware and Mobile Computing-based E-Commerce Product Recommendation System

Application and Analysis of Artificial Intelligence in College Students’ Career Planning and Employment and Entrepreneurship Information Recommendation

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