Identifying and recommending user-interested attributes with values

Yun Shan Cheng, Ping Yu Hsu, Yu Chin Liu

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)765-781
Number of pages17
JournalIndustrial Management and Data Systems
Volume118
Issue number4
DOIs
StatePublished - 2018

Keywords

  • Attribute selection
  • Means-end chain theory
  • Recommender system
  • Values

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