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: Walailak Kamlor, Kenneth Cosh
Format: Conference Proceeding
Published: 2018
Online Access:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84902440612&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/45332
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Institution: Chiang Mai University
id th-cmuir.6653943832-45332
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spelling th-cmuir.6653943832-453322018-01-24T06:08:43Z Product discovery via recommendation based on user comments Walailak Kamlor Kenneth Cosh 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. 2018-01-24T06:08:43Z 2018-01-24T06:08:43Z 2014-01-01 Conference Proceeding 2-s2.0-84902440612 https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84902440612&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/45332
institution Chiang Mai University
building Chiang Mai University Library
country Thailand
collection CMU Intellectual Repository
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 Proceeding
author Walailak Kamlor
Kenneth Cosh
spellingShingle Walailak Kamlor
Kenneth Cosh
Product discovery via recommendation based on user comments
author_facet Walailak Kamlor
Kenneth Cosh
author_sort Walailak Kamlor
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
publishDate 2018
url https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84902440612&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/45332
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