Extracting Attributes for Recommender Systems Based on MEC Theory

Yun Shan Cheng; Ping Yu Hsu; Yu Chin Liu

doi:10.1109/CCOMS.2018.8463179

Extracting Attributes for Recommender Systems Based on MEC Theory

Yun Shan Cheng, Ping Yu Hsu, Yu Chin Liu

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

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 for text based documents, there is little theoretic background information guiding the selection of elements. On the other hand, Means-End Chain theory noted deciding elements that dictate product selection include attributes, benefits, and values. This study strives to establish a methodology to identify favorite attributes based on Means-End Chain theory. The experiment is conducted to compare and contrast the performance of the proposed method and two traditional content (attribute) based methodologies. The results show that the proposed system outperforms the two methods by 82% and 68%, respectively.

Original language	English
Title of host publication	2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	125-129
Number of pages	5
ISBN (Print)	9781538663509
DOIs	https://doi.org/10.1109/CCOMS.2018.8463179
State	Published - 11 Sep 2018
Event	3rd International Conference on Computer and Communication Systems, ICCCS 2018 - Nagoya, Japan Duration: 27 Apr 2018 → 30 Apr 2018

Publication series

Name	2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018

Conference

Conference	3rd International Conference on Computer and Communication Systems, ICCCS 2018
Country/Territory	Japan
City	Nagoya
Period	27/04/18 → 30/04/18

Keywords

content based recommender system
mean-end chain theory

Access to Document

10.1109/CCOMS.2018.8463179

Cite this

Cheng, Y. S., Hsu, P. Y., & Liu, Y. C. (2018). Extracting Attributes for Recommender Systems Based on MEC Theory. In 2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018 (pp. 125-129). Article 8463179 (2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCOMS.2018.8463179

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Cheng, YS, Hsu, PY & Liu, YC 2018, Extracting Attributes for Recommender Systems Based on MEC Theory. in 2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018., 8463179, 2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018, Institute of Electrical and Electronics Engineers Inc., pp. 125-129, 3rd International Conference on Computer and Communication Systems, ICCCS 2018, Nagoya, Japan, 27/04/18. https://doi.org/10.1109/CCOMS.2018.8463179

Extracting Attributes for Recommender Systems Based on MEC Theory. / Cheng, Yun Shan; Hsu, Ping Yu; Liu, Yu Chin.
2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 125-129 8463179 (2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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N2 - 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 for text based documents, there is little theoretic background information guiding the selection of elements. On the other hand, Means-End Chain theory noted deciding elements that dictate product selection include attributes, benefits, and values. This study strives to establish a methodology to identify favorite attributes based on Means-End Chain theory. The experiment is conducted to compare and contrast the performance of the proposed method and two traditional content (attribute) based methodologies. The results show that the proposed system outperforms the two methods by 82% and 68%, respectively.

AB - 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 for text based documents, there is little theoretic background information guiding the selection of elements. On the other hand, Means-End Chain theory noted deciding elements that dictate product selection include attributes, benefits, and values. This study strives to establish a methodology to identify favorite attributes based on Means-End Chain theory. The experiment is conducted to compare and contrast the performance of the proposed method and two traditional content (attribute) based methodologies. The results show that the proposed system outperforms the two methods by 82% and 68%, respectively.

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Cheng YS, Hsu PY, Liu YC. Extracting Attributes for Recommender Systems Based on MEC Theory. In 2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 125-129. 8463179. (2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018). doi: 10.1109/CCOMS.2018.8463179

Extracting Attributes for Recommender Systems Based on MEC Theory

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Keywords

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