@inproceedings{3db3327ac21a4cd599abbc7fba6bd2bf,
title = "Extracting Attributes for Recommender Systems Based on MEC Theory",
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.",
keywords = "content based recommender system, mean-end chain theory",
author = "Cheng, {Yun Shan} and Hsu, {Ping Yu} and Liu, {Yu Chin}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 3rd International Conference on Computer and Communication Systems, ICCCS 2018 ; Conference date: 27-04-2018 Through 30-04-2018",
year = "2018",
month = sep,
day = "11",
doi = "10.1109/CCOMS.2018.8463179",
language = "???core.languages.en_GB???",
isbn = "9781538663509",
series = "2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "125--129",
booktitle = "2018 3rd International Conference on Computer and Communication Systems, ICCCS 2018",
}