An interval-based Recommender System with Implicit Feedback
碩士 === 國立中央大學 === 企業管理研究所 === 99 === The e-commerce is booming through rapid expansion of Internet. However, consumers can find many products and related information from the Internet, but also they face a problem to choose their right Internet within short time. Enterprises usually rely on a reco...
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ndltd-TW-099NCU051210352017-07-06T04:42:56Z http://ndltd.ncl.edu.tw/handle/38301907600219816439 An interval-based Recommender System with Implicit Feedback 運用隱性回饋趨勢推薦產品方法之研究 Szu-Chi Chao 趙思棋 碩士 國立中央大學 企業管理研究所 99 The e-commerce is booming through rapid expansion of Internet. However, consumers can find many products and related information from the Internet, but also they face a problem to choose their right Internet within short time. Enterprises usually rely on a recommender system to strengthen the relationship with consumers, such as saving consumers’ search time, improving customer satisfaction and tracking the consumer behavior. Through recommender system enterprises can analyze the consumer behavior and product preferences and can provide the right goods to consumers to enhance the consumer''s purchase intention. However, most of the traditional recommender systems are static which identify consumers’ preferences by analyzing their historical data of the certain time period and those methods don’t consider a time factor that consumers’ preferences might change over time. This study propose a time-based content based approach for recommendation that considers time factors to the operation of the recommender system to improve the efficiency of the traditional recommender systems. The purpose of this study is to recommend the products that meet the consumers’ demand, by observing the changes of consumers’ transactions over time and identifying trends in consumers’ interests. We establish a model to convert the consumer transactions to the degree of preference and combine with the time factor. By using this model, we analyze trends in the consumer purchase interests and provide personal recommendation efficiently. This study uses the mobile service transaction database to assess the performance of the proposed approach. The experiment result shows that the proposed approach can predict the consumers’ purchase interest efficiently in the products’ category level. Ping-Yu Hsu 許秉瑜 2011 學位論文 ; thesis 56 zh-TW |
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碩士 === 國立中央大學 === 企業管理研究所 === 99 === The e-commerce is booming through rapid expansion of Internet. However, consumers can find many products and related information from the Internet, but also they face a problem to choose their right Internet within short time. Enterprises usually rely on a recommender system to strengthen the relationship with consumers, such as saving consumers’ search time, improving customer satisfaction and tracking the consumer behavior. Through recommender system enterprises can analyze the consumer behavior and product preferences and can provide the right goods to consumers to enhance the consumer''s purchase intention. However, most of the traditional recommender systems are static which identify consumers’ preferences by analyzing their historical data of the certain time period and those methods don’t consider a time factor that consumers’ preferences might change over time.
This study propose a time-based content based approach for recommendation that considers time factors to the operation of the recommender system to improve the efficiency of the traditional recommender systems. The purpose of this study is to recommend the products that meet the consumers’ demand, by observing the changes of consumers’ transactions over time and identifying trends in consumers’ interests. We establish a model to convert the consumer transactions to the degree of preference and combine with the time factor. By using this model, we analyze trends in the consumer purchase interests and provide personal recommendation efficiently. This study uses the mobile service transaction database to assess the performance of the proposed approach. The experiment result shows that the proposed approach can predict the consumers’ purchase interest efficiently in the products’ category level.
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author2 |
Ping-Yu Hsu |
author_facet |
Ping-Yu Hsu Szu-Chi Chao 趙思棋 |
author |
Szu-Chi Chao 趙思棋 |
spellingShingle |
Szu-Chi Chao 趙思棋 An interval-based Recommender System with Implicit Feedback |
author_sort |
Szu-Chi Chao |
title |
An interval-based Recommender System with Implicit Feedback |
title_short |
An interval-based Recommender System with Implicit Feedback |
title_full |
An interval-based Recommender System with Implicit Feedback |
title_fullStr |
An interval-based Recommender System with Implicit Feedback |
title_full_unstemmed |
An interval-based Recommender System with Implicit Feedback |
title_sort |
interval-based recommender system with implicit feedback |
publishDate |
2011 |
url |
http://ndltd.ncl.edu.tw/handle/38301907600219816439 |
work_keys_str_mv |
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