Similarity Measure for Product Attribute Estimation
Representing products as a combination of properties that capture the essence of consumer sentiment is critical for companies that strive to understand consumer behavior. A catalogue of products described in terms of their attributes could offer companies a wide range of benefits; from improving exi...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9206586/ |
| _version_ | 1818873608668184576 |
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| author | Patricia Ortal Masato Edahiro |
| author_facet | Patricia Ortal Masato Edahiro |
| author_sort | Patricia Ortal |
| collection | DOAJ |
| description | Representing products as a combination of properties that capture the essence of consumer sentiment is critical for companies that strive to understand consumer behavior. A catalogue of products described in terms of their attributes could offer companies a wide range of benefits; from improving existing products or developing new ones, to improving the quality of site search and offering better item recommendations to users. In this paper, we propose a method that encodes products as a sequence of attributes, each of which represents a different dimension of the consumer perception. In the proposed method, first, a base product set with known attribute values is built based on consumers' perceptions. Then, new product attribute vectors are estimated using product similarity. The proposed method also incorporates a new similarity measure that is based on purchase behavior and which is suitable for estimating product attribute vector distances. Because it takes into account the magnitude of the individual components of the vectors under comparison, the proposed method is free from the limitations of conventional similarity measures. The results of experiments conducted using real-world data indicate that the proposed method has superior performance compared to conventional approaches in terms of mean absolute error (MAE) and root mean squared error (RMSE). |
| first_indexed | 2024-12-19T12:57:25Z |
| format | Article |
| id | doaj.art-cc02576ca8524544bb96273c1c22fadb |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T12:57:25Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-cc02576ca8524544bb96273c1c22fadb2022-12-21T20:20:22ZengIEEEIEEE Access2169-35362020-01-01817907317908210.1109/ACCESS.2020.30270239206586Similarity Measure for Product Attribute EstimationPatricia Ortal0https://orcid.org/0000-0002-3097-0505Masato Edahiro1https://orcid.org/0000-0003-2188-2690Rakuten Institute of Technology, Rakuten, Inc., Tokyo, JapanGraduate School of Information Science, Nagoya University, Nagoya, JapanRepresenting products as a combination of properties that capture the essence of consumer sentiment is critical for companies that strive to understand consumer behavior. A catalogue of products described in terms of their attributes could offer companies a wide range of benefits; from improving existing products or developing new ones, to improving the quality of site search and offering better item recommendations to users. In this paper, we propose a method that encodes products as a sequence of attributes, each of which represents a different dimension of the consumer perception. In the proposed method, first, a base product set with known attribute values is built based on consumers' perceptions. Then, new product attribute vectors are estimated using product similarity. The proposed method also incorporates a new similarity measure that is based on purchase behavior and which is suitable for estimating product attribute vector distances. Because it takes into account the magnitude of the individual components of the vectors under comparison, the proposed method is free from the limitations of conventional similarity measures. The results of experiments conducted using real-world data indicate that the proposed method has superior performance compared to conventional approaches in terms of mean absolute error (MAE) and root mean squared error (RMSE).https://ieeexplore.ieee.org/document/9206586/Attribute estimationcollaborative filteringconsumer behaviore-commercesimilarity measures |
| spellingShingle | Patricia Ortal Masato Edahiro Similarity Measure for Product Attribute Estimation IEEE Access Attribute estimation collaborative filtering consumer behavior e-commerce similarity measures |
| title | Similarity Measure for Product Attribute Estimation |
| title_full | Similarity Measure for Product Attribute Estimation |
| title_fullStr | Similarity Measure for Product Attribute Estimation |
| title_full_unstemmed | Similarity Measure for Product Attribute Estimation |
| title_short | Similarity Measure for Product Attribute Estimation |
| title_sort | similarity measure for product attribute estimation |
| topic | Attribute estimation collaborative filtering consumer behavior e-commerce similarity measures |
| url | https://ieeexplore.ieee.org/document/9206586/ |
| work_keys_str_mv | AT patriciaortal similaritymeasureforproductattributeestimation AT masatoedahiro similaritymeasureforproductattributeestimation |