近年來，隨著網際網路的成長，人們可以透過網路購物，即便不出門也能購買像要的商品。在台灣，蝦皮、露天、PC-home等都是常見知名的購物網站。一般來說，目前網路購物提供使用者不少便利的功能，有搜尋、熱門關鍵字、商品分類等，都是相當實用。然而仍有很多的功能現在的網路購物仍難以達成，比如當我們外出購物的時候，可以從店員、銷售員等得到一些建議或是相關知識。本論文為了能給消費者一些購物的意見及相關知識而實作購物機器人。
本論文提出了ATCN模型，使用購物文章來做資料的訓練。ATCN 模型是一個基於複雜任務架構，共四層的模型。ATCN模型訓練完後，會生成4個資料庫，我們利用這些資料庫來建構購物機器人。
本論文有兩個實驗，一個是評估任務抓取模組的表現，另一個是評估預測相關任務模組的表現。我們相信購物機器人在未來會更加的便利，這代表著使用者能花更少的時間跟體力在商品的購買上。

Nowadays, many people can buy things online without going out. In Taiwan, auction sites, such as Ruten, PC-HOME, are well-known. Many people buy what he or she wants through online channels. Some functions, like the searching bar, common searching words, products categories, …, is very useful. However, we can’t get the advices and recommends via online shopping. When we go outside to buy things, we often get advice from sellers. However, if we buy things online, we can only search data by ourselves. So, we want to create a shopping chatbot to provide users some advices when they are shopping.
We propose the ATCN model, Activity-Task-Consumption Need model, to train the data using shopping articles. ATCN model is based on complex structure, in which there are four layers. We use four database tables, which produced by ATCN model, to build the shopping chatbot.
We have two experiments, one is to evaluate the performance of task extraction and the other is to evaluate the performance of related task prediction.
We think the shopping chatbot will be more convenient soon. We can use less time and effort in shopping.

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