Shopping Chatbot based on Complex Task Structure and Consumption Need

碩士 === 國立成功大學 === 資訊工程學系 === 107 === 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...

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Bibliographic Details
Main Authors: Chen-AnWang, 王振安
Other Authors: Wen-Hsiang Lu
Format: Others
Language:en_US
Published: 2019
Online Access:http://ndltd.ncl.edu.tw/handle/e9vcnn
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spelling ndltd-TW-107NCKU53921022019-10-26T06:24:19Z http://ndltd.ncl.edu.tw/handle/e9vcnn Shopping Chatbot based on Complex Task Structure and Consumption Need 基於複雜任務結構與消費需求之購物機器人 Chen-AnWang 王振安 碩士 國立成功大學 資訊工程學系 107 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. Wen-Hsiang Lu 盧文祥 2019 學位論文 ; thesis 39 en_US
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language en_US
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description 碩士 === 國立成功大學 === 資訊工程學系 === 107 === 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.
author2 Wen-Hsiang Lu
author_facet Wen-Hsiang Lu
Chen-AnWang
王振安
author Chen-AnWang
王振安
spellingShingle Chen-AnWang
王振安
Shopping Chatbot based on Complex Task Structure and Consumption Need
author_sort Chen-AnWang
title Shopping Chatbot based on Complex Task Structure and Consumption Need
title_short Shopping Chatbot based on Complex Task Structure and Consumption Need
title_full Shopping Chatbot based on Complex Task Structure and Consumption Need
title_fullStr Shopping Chatbot based on Complex Task Structure and Consumption Need
title_full_unstemmed Shopping Chatbot based on Complex Task Structure and Consumption Need
title_sort shopping chatbot based on complex task structure and consumption need
publishDate 2019
url http://ndltd.ncl.edu.tw/handle/e9vcnn
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AT wángzhènān jīyúfùzárènwùjiégòuyǔxiāofèixūqiúzhīgòuwùjīqìrén
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