Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1108/IMDS-04-2017-0164 | INDUSTRIAL MANAGEMENT & DATA SYSTEMS |
Keywords | Field | DocType |
Values, Recommender system, Attribute selection, Means-end chain theory | Recommender system,Laddering,Content analysis,Information retrieval,Feature selection,Originality,Computer-assisted web interviewing,Purchasing,Engineering,Cold start (automotive),Management science | Journal |
Volume | Issue | ISSN |
118 | 4 | 0263-5577 |
Citations | PageRank | References |
1 | 0.40 | 31 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yun-Shan Cheng | 1 | 1 | 0.74 |
| Ping-Yu Hsu | 2 | 276 | 41.77 |
| Yu-Chin Liu | 3 | 12 | 3.96 |