Identifying and recommending user-interested attributes with values

Yun Shan Cheng, Ping Yu Hsu, Yu Chin Liu

研究成果: 雜誌貢獻期刊論文同行評審

10 引文 斯高帕斯(Scopus)

摘要

Purpose: To retain consumer attention and increase purchasing rates, many e-commerce vendors have adopted content-based recommender systems. However, apart from text-based documents, there is little theoretical background guiding element selection, resulting in a limited content analysis problem. Another inherent problem is overspecialization. The purpose of this paper is to establish a value-based recommendation methodology for identifying favorable attributes, benefits, and values on the basis of means-end chain theory. The identified elements and the relationships between them were utilized to construct a recommender system without incurring either problem. Design/methodology/approach: This study adopted soft laddering and content analysis to collect popular elements. The relationships between the elements were established by using a hard laddering online questionnaire. The elements and the relationships were utilized to build a hierarchical value map (HVM). A mathematical model was then devised on the basis of the HVM to predict user preferences of attributes. Findings: The results of a performance comparison showed that the proposed method outperformed the content-based attribute recommendation method and a hybrid method by 39 and 68 percent, respectively. Originality/value: Although hybrid methods have been proposed to resolve the problem of overspecialization in content-based recommender systems, such methods have incurred “cold start” and “sparsity” problems. The proposed method can provide recommendations without causing these problems while outperforming the content-based and hybrid approaches.

原文???core.languages.en_GB???
頁(從 - 到)765-781
頁數17
期刊Industrial Management and Data Systems
118
發行號4
DOIs
出版狀態已出版 - 2018

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