使用者消費行為對於研究者來說一直都是充滿趣味及挑戰性的研究領域，而這也是大
多數網站獲利的主要來源。智慧型手機擁有感知人類周遭環境的能力，隨著智慧型手機在
人類的生活中越來越不可或缺，這樣的能力也讓我們有機會了解使用者的環境以及意圖。
在這篇論文當中，我們將會提出一個智慧型手機消費者行為預測框架，藉由手機所能敢知
道的資訊，獲取人的環境資料，並分析這些資料得到使用者實際的購物意圖。藉由其中三
個關鍵項目，分別是使用者消費行為的收集、消費者行為意圖分析以及消費者行為預測模
型而得到準確的預測。為了證明此框架的可行性，我們在Android 手機上開發了Context
收集程式，並邀請14 個受測者，並讓每位受測者進行3 個禮拜的連續測試。在這三個禮
拜中，使用者分別拿著悠遊卡進行所有消費並記錄，我們也透過使用者的智慧型手機感知
使用者的周遭環境及意圖。最後將這些資料進行分析並丟進消費者行為預測框架進行預測，
整個系統可以達到百分之六十七的準確度。

Consuming activities is one of the most interesting behaviors for researchers, which is
major profitable source to online web service. As smartphone have become prevalent and
have the ability to sense user context, mobile offers a great opportunity to understand
user consuming intention. In this thesis, we present a next consuming behavior prediction
framework for smartphone users. We predict user next possible purchase item by collecting
useful context from smartphone and extract semantic context for further study. As one of
the key enabling techniques, a probabilistic prediction model has proposed to better describe
user consuming behavior. To demonstrate the feasibility of proposed framework, we evaluate
the overall framework by constructing a context collection daemon in Android and asking
14 participants conduct a 3 weeks experiment by using Easycard in every transaction during
daily life. The result indicates that timing and location are the most important context
for next consuming activity prediction and our framework reach 76% accuracy in overall
evaluation.
1

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