| dc.contributor | 資管系 | en_US |
| dc.creator (作者) | 林靖;許建隆;李有仁 | zh_TW |
| dc.creator (作者) | Lin, Arthur J.;Hsu, Chien-Lung;Li, Eldon Y. | en_US |
| dc.date (日期) | 2014.08 | en_US |
| dc.date.accessioned | 12-Aug-2014 15:46:21 (UTC+8) | - |
| dc.date.available | 12-Aug-2014 15:46:21 (UTC+8) | - |
| dc.date.issued (上傳時間) | 12-Aug-2014 15:46:21 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/68621 | - |
| dc.description.abstract (摘要) | Providing experience-oriented offerings through e-commerce is an issue increasing critical in the growing commoditization of e-commercial services. The high accuracy of predictions rendered by Recommendation System (RS) technologies has strengthened the opportunities for experience-oriented offerings, making RS application an effective way of assisting consumers in online decision-making. This study proposes a RS for movie lovers using neural networks in collaborative filtering systems for consumers’ experiential decisions. The experimental results reveal that it not only improves the accuracy of predicting movie ratings but also increases data transfer rates and provides richer user experiences. | en_US |
| dc.format.extent | 958800 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | Expert Systems with Applications, 41(10), 4904-4914 | en_US |
| dc.source.uri (資料來源) | http://dx.doi.org/10.1016/j.eswa.2014.01.035 | en_US |
| dc.subject (關鍵詞) | Recommendation system;Experiential decision;Multilayer perception model;Neural network system;Collaborative filtering system | en_US |
| dc.title (題名) | Improving the Effectiveness of Experiential Decisions by Recommendation Systems | en_US |
| dc.type (資料類型) | article | en |
