B2C網站商品推薦模式精進之研究 : 以M購物網為例

機構典藏 > College of Business and Management > Graduate Institute & Department of Information Management > Thesis > Item 987654321/105501

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Title:	B2C網站商品推薦模式精進之研究 : 以M購物網為例
Other Titles:	A study on the improvement of B2C website product recommendation system : an example of M online shopping website
Authors:	張懋萱;Chang, Mao-Hsuan
Contributors:	淡江大學資訊管理學系碩士在職專班黃明達;Hwang, Ming-Dar
Keywords:	推薦系統;個人化推薦;個人化促銷;電子商務;Recommendation Systems;Personalized Recommendation;Personalized Promotion;e-commerce
Date:	2015
Issue Date:	2016-01-22 14:57:37 (UTC+8)
Abstract:	本研究個案現有推薦系統產生之營收占比約5%，仍有成長空間。為提升推薦績效，本研究透過個案交易資料分析，探討促銷活動對整體營收之影響，推導出以促銷商品價格優惠活動做為新的推薦因子，並將此推薦因子加入現有推薦系統中，基於推薦系統具有個人化的特性，進而形成個人化促銷商品推薦。實際驗證後發現在具有價格優惠的因素下，針對個別消費者推薦適合的促銷商品，推薦營收占比由5%提升至6.51%，成長率30%;訂單轉換率由1.74%提升至2.15%，成長率23%。除證明價格促銷對於消費者具有刺激其完成交易的影響力之外，也證明價格促銷運用在個人化推薦系統上，具有提升推薦績效的效果。 This case study shows that the current recommendation system contributes to approximately 5% of the total revenue and that there is scope to increase this percentage. To enhance the performance of the recommendation system, this study analyzed trading data and investigated the effect of promotional activities on the overall revenue. Moreover, it identified the pricing of promotional items as a new recommendation factor and incorporated this factor into the existing recommendation system. The system is capable of customer personalization; hence, it can recommend unique products to individual customers. The study found that with the addition of the price promotion factor, recommending appropriate promotional products to individual consumers led to an increase in the percentage of revenue resulting from recommendations ranging from 5% to 6.51% as well as an increase in the order conversion rate ranging from 1.74% to 2.15%. The findings provide evidence for the argument that price promotions stimulate the completion of transaction processes in the purchase-decision process of consumers. Furthermore, the study shows that price promotions can be applied to personalized recommendation systems for increasing the percentage of revenue resulting from such recommendations.
Appears in Collections:	[Graduate Institute & Department of Information Management] Thesis

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