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  • 學位論文

發展產品獲利性考量之推薦系統

The Development of Product Profitability Based Recommender Systems

指導教授 : 陳穆臻
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摘要


目前既有的推薦系統做產品推薦時僅考慮產品的購買機率(product purchase probability),而忽略了企業應用推薦系統的同時亦希望追求獲利增加。因此本研究探討以企業(賣方)與顧客(買方)為不同出發點做考量之數種推薦系統。由賣方的角度而言,係依據整體產品購買機率(overall product purchase probability)與產品獲利性(product profitability)做推薦;由買方的角度而言,係依據個別顧客的偏好(individual customer’s preferences)做推薦。 本研究嘗試以推薦準確率(recommendation accuracy)與由交叉銷售所得利潤(profit from cross-selling)為評估指標比較四種不同觀點的推薦系統。依據實驗結果,可做出以下結論: 1. 就非個人化推薦而言,額外考量產品獲利性會使推薦準確率下降。 2. 與非個人化推薦比較時,只從個別消費者的偏好觀點做推薦會使推薦準確率明顯提高。 3. 就個人化推薦而言,額外考量產品獲利性使得交叉銷售所得利潤增加,但推薦準確率並無顯著差異。 4. 由個人化的觀點做推薦會使推薦準確率明顯提高。

並列摘要


This research attempts to analyze several recommender systems based on the perspectives of sellers and buyers. From the seller’s perspective, recommendations are made based on the overall product purchase probability and the product profitability; from the buyer’s perspective, recommendations are made based on individual customer’s preferences. Four recommender systems are compared in terms of recommendation accuracy and/or profit from cross-selling. Based on the experimental results, the following conclusions can be made: 1. As far as non-personalized recommendations are concerned, the recommendation accuracy will decrease when product profitability is additionally taken into consideration. 2. Compared with non-personalized recommendations, the recommendation accuracy will significantly increase when recommendations are made only from the buyer’s preference perspective. 3. As far as personalized recommendations are concerned, the profit from cross-selling significantly increases and the recommendation accuracy is only slightly different when product profitability is additionally taken into consideration. 4. The recommendation accuracy will significantly increase when recommendations are made in personalized approaches.

參考文獻


[2] Anderson, K. and Kerr C., 2002, Customer Relationship Management, McGraw-Hill.
[4] Ansari, A., Essegaier, S. and Kohli, R., 2000, Internet recommendation systems, Journal of Marketing Research, 37(3), 363-375.
[5] Basilico, J. and Hofmann, T., 2004, Unifying Collaborative and Content-Based Filtering, Proceedings of the 21st International Conference on Machine Learning.
[6] Carenini, G.., Smith, J. and Poole, D., 2003, Towards more conversational and collaborative recommender systems, Proceedings of the IUI’03, Miami, FL, 12-18.
[7] Chalmers, M., Rodden, K. and Brodbeck, D., 1998, The order of things: activity-centered information access, Computer Networks and ISDN Systems, 359-367.

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