Product discovery via recommendation based on user comments

Recommendation systems on E-commerce websites help consumers to find products. A recommendation system learns consumer behavior in order to suggest products to those consumers. Recommendation systems allow consumers to have new experiences discovering new products rather than needing to search for t...

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Main Authors: Kamlor W., Cosh K.
Format: Conference or Workshop Item
Language:English
Published: IEEE Computer Society 2014
Online Access:http://www.scopus.com/inward/record.url?eid=2-s2.0-84902440612&partnerID=40&md5=49de15c2c488b21836928e5a6aee67b5
http://cmuir.cmu.ac.th/handle/6653943832/1260
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Institution: Chiang Mai University
Language: English
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spelling th-cmuir.6653943832-12602014-08-29T09:29:00Z Product discovery via recommendation based on user comments Kamlor W. Cosh K. Recommendation systems on E-commerce websites help consumers to find products. A recommendation system learns consumer behavior in order to suggest products to those consumers. Recommendation systems allow consumers to have new experiences discovering new products rather than needing to search for them. When making purchase decisions consumers often use the comments left by previous buyers to help them. This paper presents how recommendation systems help E-commerce websites to recommend products, analyzes the recommendations used on some example sites and presents a new technique for recommendations based on the analysis of user comments and then analyzes the results of the new technique. The new techniques include parsing the text in comments to generate a word cloud based on the log likelihood of word frequencies, and then compares products using the RV Coefficient. Our approach automatically identifies similar products for recommendation, and based on the results of our experiment, the recommendations closely match those that would be manually chosen. © 2013 IEEE. 2014-08-29T09:29:00Z 2014-08-29T09:29:00Z 2014 Conference Paper 9781479914234 105705 http://www.scopus.com/inward/record.url?eid=2-s2.0-84902440612&partnerID=40&md5=49de15c2c488b21836928e5a6aee67b5 http://cmuir.cmu.ac.th/handle/6653943832/1260 English IEEE Computer Society
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
language English
description Recommendation systems on E-commerce websites help consumers to find products. A recommendation system learns consumer behavior in order to suggest products to those consumers. Recommendation systems allow consumers to have new experiences discovering new products rather than needing to search for them. When making purchase decisions consumers often use the comments left by previous buyers to help them. This paper presents how recommendation systems help E-commerce websites to recommend products, analyzes the recommendations used on some example sites and presents a new technique for recommendations based on the analysis of user comments and then analyzes the results of the new technique. The new techniques include parsing the text in comments to generate a word cloud based on the log likelihood of word frequencies, and then compares products using the RV Coefficient. Our approach automatically identifies similar products for recommendation, and based on the results of our experiment, the recommendations closely match those that would be manually chosen. © 2013 IEEE.
format Conference or Workshop Item
author Kamlor W.
Cosh K.
spellingShingle Kamlor W.
Cosh K.
Product discovery via recommendation based on user comments
author_facet Kamlor W.
Cosh K.
author_sort Kamlor W.
title Product discovery via recommendation based on user comments
title_short Product discovery via recommendation based on user comments
title_full Product discovery via recommendation based on user comments
title_fullStr Product discovery via recommendation based on user comments
title_full_unstemmed Product discovery via recommendation based on user comments
title_sort product discovery via recommendation based on user comments
publisher IEEE Computer Society
publishDate 2014
url http://www.scopus.com/inward/record.url?eid=2-s2.0-84902440612&partnerID=40&md5=49de15c2c488b21836928e5a6aee67b5
http://cmuir.cmu.ac.th/handle/6653943832/1260
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