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DC Field | Value | Language |
---|---|---|
dc.contributor.author | DIe Hu | en_US |
dc.contributor.author | Danaitun Pongpatcharatrontep | en_US |
dc.contributor.author | Sirikorn Santirojanakul | en_US |
dc.contributor.author | Achara Khamaksorn | en_US |
dc.date.accessioned | 2022-05-27T08:25:53Z | - |
dc.date.available | 2022-05-27T08:25:53Z | - |
dc.date.issued | 2022-01-01 | en_US |
dc.identifier.other | 2-s2.0-85127600461 | en_US |
dc.identifier.other | 10.1109/ECTIDAMTNCON53731.2022.9720413 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85127600461&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/72460 | - |
dc.description.abstract | With the tremendous development of the Internet, e-commerce and social networks, research on Social Recommendation Systems has become a hot topic to solve information overload and respond to diverse individual demands. However, many of the existing social recommendation methods lack an in-depth analysis of long-stay users from cross-cultural backgrounds, which causes insufficient social recommendations, since the social attributes and requirements of these users differ from those of short-stay tourists, local organisations or residents. Therefore, taking Chinese long-stayers in Chiang Mai as a case study, this paper follows the Knowledge Management process with the aim of producing a social recommendation framework based on social networks to provide Chinese long-stayers with filtered and efficient social recommendations. The research employs qualitative and quantitative methods to investigate the research problems and conducts online surveys and in-depth interviews to collect the individual and collective data and analyse them using Social Network Analysis. The proposed social recommendation framework aims to benefit Chinese long-stayers and any other organisations that need social recommendations or innovative business and management strategies in relation to Chinese long-stayers in Chiang Mai. The preliminary work will contribute to the current accumulative knowledge of the impact of social networks of Chinese long-stayers on social recommendation performance in a cross-cultural context. | en_US |
dc.subject | Arts and Humanities | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Decision Sciences | en_US |
dc.subject | Engineering | en_US |
dc.title | A Social Recommendation Framework for Chinese Long-stayers in Chiang Mai | en_US |
dc.type | Conference Proceeding | en_US |
article.title.sourcetitle | 7th International Conference on Digital Arts, Media and Technology, DAMT 2022 and 5th ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering, NCON 2022 | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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