Shopping Chatbot based on Complex Task Structure and Consumption Need

Abstract

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

Date of Award	2019
Original language	English
Supervisor	Wen-Hsiang Lu (Supervisor)