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題名 SNS opinion-based recommendation for eTourism: A Taipei restaurant example
作者 趙逢毅
Chao, August F.Y.
Lai, Cheng-Yu
貢獻者 資管系
關鍵詞 Big data; Decision support systems; Internet; Product design; Semantics; Social networking (online); Websites; eTourism; Latent Semantic Analysis; Recommendation; Social networking sites; User-generated content; Case based reasoning
日期 2015-09
上傳時間 8-Aug-2017 16:49:02 (UTC+8)
摘要 By the use of Internet technology in the travel and tourism industry, tourists are considered to play more significant role in the process of planning and designing tourism-related products and services. The amount of information that can acquire from Internet may far exceed one can handle, and makes the decision considerations in the travel planning process fairly complicated. Yu [3] proposed an integrated functional framework and design process for providing web-based personalized and community decision support services, and argue to extract user experiences by using case-based reasoning. However, to construct patterns from case-based reasoning among gigantic amount of user-generated content is a heavy-loading task. In this study, we adopted latent semantic analysis (LSA) [8, 9], which is constructed language pattern and discover semantic relationship between topics in big data scenario, to recommend restaurant according to desiring for similar experience. Both academic and practical implications of proposed approach are also discussed. © Springer-Verlag Berlin Heidelberg 2015.
關聯 Communications in Computer and Information Science, 540, 393-403
2nd International Conference on Multidisciplinary Social Networks Research, MISNC 2015; Matsuyama; Japan; 1 September 2015 到 3 September 2015; 代碼 140409
資料類型 conference
DOI http://dx.doi.org/10.1007/978-3-662-48319-0_32
dc.contributor 資管系zh_Tw
dc.creator (作者) 趙逢毅zh_TW
dc.creator (作者) Chao, August F.Y.en_US
dc.creator (作者) Lai, Cheng-Yuen_US
dc.date (日期) 2015-09en_US
dc.date.accessioned 8-Aug-2017 16:49:02 (UTC+8)-
dc.date.available 8-Aug-2017 16:49:02 (UTC+8)-
dc.date.issued (上傳時間) 8-Aug-2017 16:49:02 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/111674-
dc.description.abstract (摘要) By the use of Internet technology in the travel and tourism industry, tourists are considered to play more significant role in the process of planning and designing tourism-related products and services. The amount of information that can acquire from Internet may far exceed one can handle, and makes the decision considerations in the travel planning process fairly complicated. Yu [3] proposed an integrated functional framework and design process for providing web-based personalized and community decision support services, and argue to extract user experiences by using case-based reasoning. However, to construct patterns from case-based reasoning among gigantic amount of user-generated content is a heavy-loading task. In this study, we adopted latent semantic analysis (LSA) [8, 9], which is constructed language pattern and discover semantic relationship between topics in big data scenario, to recommend restaurant according to desiring for similar experience. Both academic and practical implications of proposed approach are also discussed. © Springer-Verlag Berlin Heidelberg 2015.en_US
dc.format.extent 1309351 bytes-
dc.format.mimetype application/pdf-
dc.relation (關聯) Communications in Computer and Information Science, 540, 393-403en_US
dc.relation (關聯) 2nd International Conference on Multidisciplinary Social Networks Research, MISNC 2015; Matsuyama; Japan; 1 September 2015 到 3 September 2015; 代碼 140409en_US
dc.subject (關鍵詞) Big data; Decision support systems; Internet; Product design; Semantics; Social networking (online); Websites; eTourism; Latent Semantic Analysis; Recommendation; Social networking sites; User-generated content; Case based reasoningen_US
dc.title (題名) SNS opinion-based recommendation for eTourism: A Taipei restaurant exampleen_US
dc.type (資料類型) conference
dc.identifier.doi (DOI) 10.1007/978-3-662-48319-0_32
dc.doi.uri (DOI) http://dx.doi.org/10.1007/978-3-662-48319-0_32