Mobile Personalized Recommendation Model based on Privacy Concerns and Context Analysis for the Sustainable Development of M-commerce
| Autor: | Fei-Peng Guo, Liang Xiao, Qi-Bei Lu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Computer science
media_common.quotation_subject Geography Planning and Development TJ807-830 Context (language use) 02 engineering and technology Management Monitoring Policy and Law TD194-195 Renewable energy sources World Wide Web privacy concern 020204 information systems ontology tree 0202 electrical engineering electronic engineering information engineering Collaborative filtering Quality (business) GE1-350 consumer needs context information media_common Sustainable development personalized recommendation Environmental effects of industries and plants Renewable Energy Sustainability and the Environment business.industry Mobile commerce sustainability Environmental sciences Context analysis Order (business) Sustainability ComputingMilieux_COMPUTERSANDSOCIETY 020201 artificial intelligence & image processing business hybrid collaborative filtering |
| Zdroj: | Sustainability, Vol 12, Iss 3036, p 3036 (2020) Sustainability Volume 12 Issue 7 |
| ISSN: | 2071-1050 |
| Popis: | A mobile personalized recommendation service satisfies the needs of users and stimulates them to continue to adopt mobile commerce applications. Therefore, how to precisely provide mobile personalized recommendation service is very important for the sustainable development of mobile commerce. However, privacy concerns regarding mobile commerce affect users&rsquo consumption intentions, and also reduce the quality of mobile personalized recommendation services. In order to address this issue and the existing recommendation method problem in the mobile personalized recommendation service, this paper introduces six dimensions of privacy concerns and the relevant contextual information to propose a novel mobile personalized recommendation service based on privacy concerns and context analysis. First, this paper puts forward an intensity measurement method to measure the factors that influence privacy concerns, and then realizes a user-based collaborative filtering recommendation integrated with the intensity of privacy concerns. Second, a context similarity algorithm based on a context ontology-tree is proposed, after which this study realizes a user-based collaborative filtering recommendation integrated with context similarity. Finally, the research produces a hybrid collaborative filtering recommendation integrated with privacy concerns and context information. After experimental verification, the results show that this model can effectively solve the problems of data sparseness and cold starts. More importantly, it can reduce the influence of users&rsquo privacy concerns on the adoption of mobile personalized recommendation services, and promote the sustainable development of mobile commerce. |
| Databáze: | OpenAIRE |
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