近年來，電子商務正以前所未有的速度在世界各地持續增長，而隨著網路購物普及與資訊爆炸，應用於電子商務的個性化推薦系統變得愈加不可或缺。個性化推薦系統旨在幫助顧客根據其先前的行為（例如購買型態和歷史評分），在多種商品類別中有效地找到期望的產品。然而大多數的電子商務推薦系統皆採用二進制（購買/非購買）亦或是主觀的加權方法來表示顧客對於商品之偏好，但由於顧客的興趣變化迅速，故難以精確地預測其輪廓。
本研究針對交易型資料，探討以內容導向推薦系統之傳統架構整合一新元素─反饋調整器(feedback adjuster)，提出一個性化的電子商務推薦系統(PROSE)，以提高推薦品質。本方法之反饋調整器可考量顧客之重複購買行為，使其隱式回饋更能貼近現實中的喜好。
本研究之實驗使用實際交易型別資料集，分別針對所提出之PROSE進行推薦分析，並使用精確度(precision)、召回率(recall)與F1得分三種評比指標與現存的個性化推薦器系統PRES進行比較。根據實驗結果證實，本研究所提出的PROSE之性能在標準資料集中得以獲得較PRES優異之表現。

In recent years, e-commerce is growing at an unprecedented rate all over the globe. With the increasing popularity of online shopping and information explosion, personalized recommender systems for e-commerce become more and more necessary, which helps customers to find the desired products efficiently among variety of categories based on their previous behavior such as buying pattern and rating history. However, most recommender systems for e-commerce adopt binary (purchase/non-purchase) or subjective weighting methods to represent the customer preferences, which is hard to predict their profiles precisely since rapid change in tastes.
This study focuses on transactional data. A personalized recommender system for e-commerce (PROSE) is proposed in order to enhance the quality of recommendations by integrating the architecture of traditional content-based recommender system with a new component called feedback adjuster, which is designed to make customer implicit feedback reflects the reality of preferences as possible through taking into consideration their behavior of repeat purchase.
The proposed PROSE is utilized to perform a recommendation for an actual transactional dataset. The performance of the recommendation is compared with the existing personalized recommender system PRES using three indexes, namely, precision, recall and F1 score. The experimental results indicate that performance of the proposed PROSE is superior to PRES for the benchmark dataset.

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