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 proceedingConference contributionpeer-review

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 languageEnglish
Title of host publication2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages125-129
Number of pages5
ISBN (Print)9781538663509
DOIs
StatePublished - 11 Sep 2018
Event3rd International Conference on Computer and Communication Systems, ICCCS 2018 - Nagoya, Japan
Duration: 27 Apr 201830 Apr 2018

Publication series

Name2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018

Conference

Conference3rd International Conference on Computer and Communication Systems, ICCCS 2018
Country/TerritoryJapan
CityNagoya
Period27/04/1830/04/18

Keywords

  • content based recommender system
  • mean-end chain theory

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