Identifying and Recommending User-Interested Attributes with Values
博士 === 國立中央大學 === 企業管理學系 === 106 === To retain consumer attention and increase their purchasing rates, many online e-commerce vendors have adopted content-based approaches in their recommender systems. However, except text based documents, there are few theoretic background guiding the selection of...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/av6he8 |
| Summary: | 博士 === 國立中央大學 === 企業管理學系 === 106 === To retain consumer attention and increase their purchasing rates, many online e-commerce vendors have adopted content-based approaches in their recommender systems. However, except text based documents, there are few theoretic background guiding the selection of elements. On the other hand, Means-End Chain theory pointed out that deciding elements that dictate product selection include attributes, benefits, and values can be systematically identified. This study strived to establish a methodology to recommend favorite attributes to users based on Means-End Chain theory. Two experiments were conducted to compare and contrast the performance of the proposed method Value-Based Recommendation (VBR) and two traditional content (attribute) based methodologies.
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