The Design and Evaluation of Intelligent Personalized Product Search and Recommendation

碩士 === 輔仁大學 === 資訊管理學系 === 95 === Electronic commerce (e-commerce) has grown dramatically over the years into a business model of vital importance in the 21st century. Yet, consumers still encounter problems such as false expectation and purchase disputes while shopping online. Moreover, the buyer d...

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Main Authors: Jerry Wu, 吳政立
Other Authors: Wen-Shiu Lin
Format: Others
Language:zh-TW
Published: 2007
Online Access:http://ndltd.ncl.edu.tw/handle/44959172561694080782
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spelling ndltd-TW-095FJU003960492016-05-23T04:18:08Z http://ndltd.ncl.edu.tw/handle/44959172561694080782 The Design and Evaluation of Intelligent Personalized Product Search and Recommendation 智慧型個人化商品搜尋與推薦機制之設計與評估 Jerry Wu 吳政立 碩士 輔仁大學 資訊管理學系 95 Electronic commerce (e-commerce) has grown dramatically over the years into a business model of vital importance in the 21st century. Yet, consumers still encounter problems such as false expectation and purchase disputes while shopping online. Moreover, the buyer decision-making process is often prolonged due to the lack of purchasing assistance on the electronic commerce platforms’ end therefore wasting consumers’ time and energy. Furthermore, the inability of majority of online stores to understand consumers’ needs creates obstacles in providing personalized products and service efficiently. This research’s goal is to design an intelligent system based on Web2.0’s fundamental structure to assign objective, pluralistic and multilevel product information to goods through collective intelligence. The innovative Tag Vectorial Model and Folksonomy-based Ranking Algorithm are proposed to combine with Collaboration Recommendation Rules to capture user preference in providing more accurate personalized products and service. Results show, the increase of the numbers of searches and ratings conducted improves the system’s accuracy in capturing user preference and in turn allows the system to present more satisfactory product search results to consumers, effectively assisting them to narrow down product choices thus shortening the buyer decision-making process. It is also shown, the system frequently recommends products surprising to consumers. At last, after analyzing Tag Vectorial Model with graphs, it is discovered that Tag Vectorial Model is indeed able to present consumers’ motivations. This may help online businesses to further understand their consumers and provide personalized products and service to them. Subsequently, new business opportunities may be created. Wen-Shiu Lin 林文修 2007 學位論文 ; thesis 108 zh-TW
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description 碩士 === 輔仁大學 === 資訊管理學系 === 95 === Electronic commerce (e-commerce) has grown dramatically over the years into a business model of vital importance in the 21st century. Yet, consumers still encounter problems such as false expectation and purchase disputes while shopping online. Moreover, the buyer decision-making process is often prolonged due to the lack of purchasing assistance on the electronic commerce platforms’ end therefore wasting consumers’ time and energy. Furthermore, the inability of majority of online stores to understand consumers’ needs creates obstacles in providing personalized products and service efficiently. This research’s goal is to design an intelligent system based on Web2.0’s fundamental structure to assign objective, pluralistic and multilevel product information to goods through collective intelligence. The innovative Tag Vectorial Model and Folksonomy-based Ranking Algorithm are proposed to combine with Collaboration Recommendation Rules to capture user preference in providing more accurate personalized products and service. Results show, the increase of the numbers of searches and ratings conducted improves the system’s accuracy in capturing user preference and in turn allows the system to present more satisfactory product search results to consumers, effectively assisting them to narrow down product choices thus shortening the buyer decision-making process. It is also shown, the system frequently recommends products surprising to consumers. At last, after analyzing Tag Vectorial Model with graphs, it is discovered that Tag Vectorial Model is indeed able to present consumers’ motivations. This may help online businesses to further understand their consumers and provide personalized products and service to them. Subsequently, new business opportunities may be created.
author2 Wen-Shiu Lin
author_facet Wen-Shiu Lin
Jerry Wu
吳政立
author Jerry Wu
吳政立
spellingShingle Jerry Wu
吳政立
The Design and Evaluation of Intelligent Personalized Product Search and Recommendation
author_sort Jerry Wu
title The Design and Evaluation of Intelligent Personalized Product Search and Recommendation
title_short The Design and Evaluation of Intelligent Personalized Product Search and Recommendation
title_full The Design and Evaluation of Intelligent Personalized Product Search and Recommendation
title_fullStr The Design and Evaluation of Intelligent Personalized Product Search and Recommendation
title_full_unstemmed The Design and Evaluation of Intelligent Personalized Product Search and Recommendation
title_sort design and evaluation of intelligent personalized product search and recommendation
publishDate 2007
url http://ndltd.ncl.edu.tw/handle/44959172561694080782
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