research-article

Mining Product Adopter Information from Online Reviews for Improving Product Recommendation

Authors:

Wayne Xin Zhao,

Edward Y. Chang,

Xiaoming LiAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 10, Issue 3

Article No.: 29, Pages 1 - 23

https://doi.org/10.1145/2842629

Published: 09 February 2016 Publication History

Abstract

We present in this article an automated framework that extracts product adopter information from online reviews and incorporates the extracted information into feature-based matrix factorization for more effective product recommendation. In specific, we propose a bootstrapping approach for the extraction of product adopters from review text and categorize them into a number of different demographic categories. The aggregated demographic information of many product adopters can be used to characterize both products and users in the form of distributions over different demographic categories. We further propose a graph-based method to iteratively update user- and product-related distributions more reliably in a heterogeneous user--product graph and incorporate them as features into the matrix factorization approach for product recommendation. Our experimental results on a large dataset crawled from JingDong, the largest B2C e-commerce website in China, show that our proposed framework outperforms a number of competitive baselines for product recommendation.

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Cited By

Gulzar YAlwan AAbdullah RAbualkishik AOumrani M(2023)OCA: Ordered Clustering-Based Algorithm for E-Commerce Recommendation SystemSustainability10.3390/su1504294715:4(2947)Online publication date: 6-Feb-2023
https://doi.org/10.3390/su15042947
Latha YRao B(2023)Product recommendation using enhanced convolutional neural network for e-commerce platformCluster Computing10.1007/s10586-023-04053-327:2(1639-1653)Online publication date: 2-Jun-2023
https://dl.acm.org/doi/10.1007/s10586-023-04053-3
Ahmad JRehman ARauf HJaved KAlkhayyal MAlnuaim A(2022)Service Recommendations Using a Hybrid Approach in Knowledge Graph with Keyword Acceptance CriteriaApplied Sciences10.3390/app1207354412:7(3544)Online publication date: 31-Mar-2022
https://doi.org/10.3390/app12073544
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Index Terms

Mining Product Adopter Information from Online Reviews for Improving Product Recommendation
1. Information systems
  1. Information systems applications

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 10, Issue 3

February 2016

358 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/2888412

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

Issue’s Table of Contents

Copyright © 2016 ACM.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 February 2016

Accepted: 01 November 2015

Received: 01 July 2015

Published in TKDD Volume 10, Issue 3

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National Natural Science Foundation of China
National Key Basic Research Program (973 Program) of China
Innovate UK

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Cited By

Gulzar YAlwan AAbdullah RAbualkishik AOumrani M(2023)OCA: Ordered Clustering-Based Algorithm for E-Commerce Recommendation SystemSustainability10.3390/su1504294715:4(2947)Online publication date: 6-Feb-2023
https://doi.org/10.3390/su15042947
Latha YRao B(2023)Product recommendation using enhanced convolutional neural network for e-commerce platformCluster Computing10.1007/s10586-023-04053-327:2(1639-1653)Online publication date: 2-Jun-2023
https://dl.acm.org/doi/10.1007/s10586-023-04053-3
Ahmad JRehman ARauf HJaved KAlkhayyal MAlnuaim A(2022)Service Recommendations Using a Hybrid Approach in Knowledge Graph with Keyword Acceptance CriteriaApplied Sciences10.3390/app1207354412:7(3544)Online publication date: 31-Mar-2022
https://doi.org/10.3390/app12073544
Ma MRen PChen ZRen ZZhao LLiu PMa Jde Rijke M(2022)Mixed Information Flow for Cross-Domain Sequential RecommendationsACM Transactions on Knowledge Discovery from Data10.1145/348733116:4(1-32)Online publication date: 8-Jan-2022
https://dl.acm.org/doi/10.1145/3487331
Wu BHe XChen YNie LZheng KYe Y(2022)Modeling Product’s Visual and Functional Characteristics for Recommender SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.299179334:3(1330-1343)Online publication date: 1-Mar-2022
https://doi.org/10.1109/TKDE.2020.2991793
Gao JYing XXu CWang JZhang SLi Z(2021)Graph-Based Stock Recommendation by Time-Aware Relational Attention NetworkACM Transactions on Knowledge Discovery from Data10.1145/345139716:1(1-21)Online publication date: 20-Jul-2021
https://dl.acm.org/doi/10.1145/3451397
Zhang Y(2021)Research on the cultivation of College Students' cross-cultural communicative competence based on Immersive artificial intelligence multimedia technology2021 3rd International Conference on Internet Technology and Educational Informization (ITEI)10.1109/ITEI55021.2021.00028(82-86)Online publication date: Dec-2021
https://doi.org/10.1109/ITEI55021.2021.00028
Selma BNarhimene BNachida R(2021)Deep Learning for Recommender Systems: Literature Review and Perspectives2021 International Conference on Recent Advances in Mathematics and Informatics (ICRAMI)10.1109/ICRAMI52622.2021.9585931(1-7)Online publication date: 21-Sep-2021
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Zhang Y(2021)Research on the cultivation of College Students’ intercultural communicative competence based on the technology of multimedia immersion in 5G EraE3S Web of Conferences10.1051/e3sconf/202125103076251(03076)Online publication date: 15-Apr-2021
https://doi.org/10.1051/e3sconf/202125103076
Suresh ACarmel Mary Belinda M(2021)Online product recommendation system using gated recurrent unit with Broyden Fletcher Goldfarb Shanno algorithmEvolutionary Intelligence10.1007/s12065-021-00594-xOnline publication date: 25-Mar-2021
https://doi.org/10.1007/s12065-021-00594-x
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