為了吸引線上消費者的注意力以及增加其購買意願，許多線上電子商務業者紛紛採用以『內容為基礎 的推薦方法』作為其線上購物之推薦系統。然而，除了以文字為基礎的文件之外，很少理論深入探討 如何有效的篩選消費者感興趣的產品特徵。然而，根據方法目的鏈理論—消費者選擇產品的關鍵在於 其「屬性/利益/價值」。因此，本計劃擬研發一個建構於方法目的鏈理論上的演算法，來識別出消費者 偏好的產品屬性，並進一步加以推薦。本計劃預期測試兩組實驗，用以比較本計劃研發之演算法與兩 個傳統以內容為基礎的推薦方法之精確度與效能。 ;To retain consumer attention and increase their purchasing rates, many online e-commerce vendors have adopted content-based approaches in their recommender systems. However, except text based documents, there are few theoretic background guiding the selection of elements. On the other hand, Mean End Chain theory pointed out that deciding elements that dictate product selection include attributes, benefits, and values can be systematically identified. This study will strive to establish a methodology to recommend favorite attributes to users based on MEC theory. Two experiments will be conducted to compare and contrast the performance of the proposed method and two traditional content (attribute) based methodologies.
