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dc.contributor.author陳俊男en_US
dc.contributor.authorChun-Nan Chenen_US
dc.contributor.author劉敦仁en_US
dc.contributor.authorDuen-Ren Liuen_US
dc.date.accessioned2014-12-12T03:08:02Z-
dc.date.available2014-12-12T03:08:02Z-
dc.date.issued2006en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#GT009434512en_US
dc.identifier.urihttp://hdl.handle.net/11536/81688-
dc.description.abstract在這個資訊科技發達的時代,顧客的購物行為已經有很大地改變:過去顧客只能透過傳統的實體商店購買產品,然而以顧客服務為導向的企業為了顧客的便利性,紛紛提供許多虛擬通路給予顧客選擇適合他們的習慣和喜好去購買產品的管道,並且能夠節省實體通路的費用,例如透過電視購物或型錄郵購的方式購買。而且為了可以幫助顧客迅速地做出購物的選擇,企業也採用推薦系統以根據顧客的興趣和購買記錄,推薦適當的產品給予顧客並且可以藉此增加顧客滿意度。有許多方法應用於推薦系統的建置上,如協同式過濾推薦法、序列規則式推薦法等等,每一種推薦方法都各有優缺點,因此過去的文獻也提出複合式推薦法,也就是結合許多推薦方法的優點,以克服單一推薦方法的缺點以及改善推薦的效果。然而過去的推薦系統皆未考慮多通路因素對推薦效果的影響,顧客在不同通路的購買行為可能會有所不同。因此本研究結合多通路因素於推薦方法中,並分別利用許多推薦方法以驗證結合多通路因素的推薦效果是否有改善。實驗結果顯示多通路因素對推薦效果有改善。zh_TW
dc.description.abstractIn this information era, customer's purchase behavior has changed a lot. In the past, customers could only buy products in the physical channel: store. Nowadays, customer-oriented companies provide many virtual channels for customers and allow them to choose the channel that they like or get used to buy products. Companies can also reduce the expenses of physical channels with virtual channels, e.g., TV channel or catalog channel. In addition, companies use recommendation systems to recommend suitable products to customers based on their interests and buying history, so as to improve customer’s degree of satisfaction. Many methods are used to implement recommendation systems, such as collaborative filtering and the sequential rule-based recommendation method. Each method has its own merit and drawback. Past research presented the hybrid method, which combined the advantages of many recommendation methods. The hybrid method can aslo overcome the drawback of individual recommendation method and improve the effect of the recommendation. However, previous research did not consider the factor of multiple channels. Customer's purchase behavior may differ in different channels. This work combines the factor of multiple channels with recommendation methods and verifies the effect of multiple channels. The experimental results show that the recommendation methods that combine the factor of multiple channels improve the quality of recommendation.en_US
dc.language.isozh_TWen_US
dc.subject產品推薦zh_TW
dc.subject協同式過濾推薦zh_TW
dc.subject多元通路zh_TW
dc.subject顧客分群zh_TW
dc.subject序列規則zh_TW
dc.subjectProduct Recommendationen_US
dc.subjectCollaborative Filteringen_US
dc.subjectMulti-channelen_US
dc.subjectCustomer Segmentationen_US
dc.subjectSequential Ruleen_US
dc.title多通路產品推薦之研究zh_TW
dc.titleA Study of Product Recommendation in Multiple Channelsen_US
dc.typeThesisen_US
dc.contributor.department資訊管理研究所zh_TW
Appears in Collections:Thesis