short-paper

Recommendation of High Quality Representative Reviews in e-commerce

Authors:

Sudeshna Sarkar,

Muthusamy Chelliah,

Prajit Prashant Sinai NadkarniAuthors Info & Claims

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

Pages 311 - 315

https://doi.org/10.1145/3109859.3109901

Published: 27 August 2017 Publication History

Abstract

Many users of e-commerce portals commonly use customer reviews for making purchase decisions. But a product may have tens or hundreds of diverse reviews leading to information overload on the customer. The main objective of our work is to develop a recommendation system to recommend a subset of reviews that have high content score and good coverage over different aspects of the product along with their associated sentiments. We address the challenge which arises due to the fact that similar aspects are mentioned in different reviews using different natural language expressions. We use vector representations to identify mentions of similar aspects and map them with aspects mentioned in product features specifications. Review helpfulness score may act as a proxy for the quality of reviews, but new reviews do not have any helpfulness score. We address the cold start problem by using a dynamic convolutional neural network to estimate the quality score from review content. The system is evaluated on datasets from Amazon and Flipkart and is found to be more effective than the competing methods.

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Cited By

Li MYu FBai YLi Y(2024)Key product review extraction based on the RLRSA-CL algorithmExpert Systems with Applications10.1016/j.eswa.2023.121223235(121223)Online publication date: Jan-2024
https://doi.org/10.1016/j.eswa.2023.121223
Patel RThakkar PUkani V(2024)CNNRecEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108062133:PAOnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108062
Yasuo MMatsushita M(2024)Comparison of Vocabulary Features Among Multiple Data Sources for Constructing a Knowledge Base on Disaster InformationTechnologies and Applications of Artificial Intelligence10.1007/978-981-97-1711-8_10(139-150)Online publication date: 28-Mar-2024
https://doi.org/10.1007/978-981-97-1711-8_10
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Index Terms

Recommendation of High Quality Representative Reviews in e-commerce
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
      2. Lexical semantics
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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Published In

cover image ACM Conferences

RecSys '17: Proceedings of the Eleventh ACM Conference on Recommender Systems

August 2017

466 pages

ISBN:9781450346528

DOI:10.1145/3109859

General Chairs:
Paolo Cremonesi
Politecnico di Milano, Italy
,
Francesco Ricci
Free University Bozen-Bolzano, Italy
,
Program Chairs:
Shlomo Berkovsky
CSIRO, Australia
,
Alexander Tuzhilin
New York University, USA

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGCHI: ACM Special Interest Group on Computer-Human Interaction

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 27 August 2017

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Short-paper

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RecSys '17

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SIGCHI

RecSys '17: Eleventh ACM Conference on Recommender Systems

August 27 - 31, 2017

Como, Italy

Acceptance Rates

RecSys '17 Paper Acceptance Rate 26 of 125 submissions, 21%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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Cited By

Li MYu FBai YLi Y(2024)Key product review extraction based on the RLRSA-CL algorithmExpert Systems with Applications10.1016/j.eswa.2023.121223235(121223)Online publication date: Jan-2024
https://doi.org/10.1016/j.eswa.2023.121223
Patel RThakkar PUkani V(2024)CNNRecEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108062133:PAOnline publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1016/j.engappai.2024.108062
Yasuo MMatsushita M(2024)Comparison of Vocabulary Features Among Multiple Data Sources for Constructing a Knowledge Base on Disaster InformationTechnologies and Applications of Artificial Intelligence10.1007/978-981-97-1711-8_10(139-150)Online publication date: 28-Mar-2024
https://doi.org/10.1007/978-981-97-1711-8_10
YASUO MKONO YYAMANISHI RMATSUSHITA M(2023)Construction of Makeup Vocabulary Datasets for Scene Search on Makeup Movies化粧動画の工程検索を指向した化粧語彙セット構築の試みJournal of Japan Society for Fuzzy Theory and Intelligent Informatics10.3156/jsoft.35.2_64535:2(645-654)Online publication date: 15-May-2023
https://doi.org/10.3156/jsoft.35.2_645
Weng CHuang CChen YHuang Y(2023)New information search model for online reviews with the perspective of user requirementsMultimedia Tools and Applications10.1007/s11042-023-14847-782:18(28165-28185)Online publication date: 20-Feb-2023
https://doi.org/10.1007/s11042-023-14847-7
Kong LLi CGe JNg VLuo B(2022)Predicting Product Review Helpfulness – A Hybrid MethodIEEE Transactions on Services Computing10.1109/TSC.2020.304109515:4(2213-2225)Online publication date: 1-Jul-2022
https://doi.org/10.1109/TSC.2020.3041095
Hiriyannaiah SSiddesh GSrinivasa K(2022)Deep visual ensemble similarity (DVESM) approach for visually aware recommendation and search in smart communityJournal of King Saud University - Computer and Information Sciences10.1016/j.jksuci.2020.03.00934:6(2562-2573)Online publication date: Jun-2022
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Mauro NArdissono LCocomazzi SCena F(2022)Using consumer feedback from location-based services in PoI recommender systems for people with autismExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.116972199:COnline publication date: 23-May-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.116972
Patro SMishra BPanda SKumar R(2022)Fuzzy Logics Based Recommendation Systems in E-Commerce: A ReviewProceedings of 2nd International Conference on Smart Computing and Cyber Security10.1007/978-981-16-9480-6_12(107-120)Online publication date: 27-May-2022
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Wang HLiu WYin J(2022)Multi-Task Learning with Personalized Transformer for Review RecommendationWeb Information Systems Engineering – WISE 202110.1007/978-3-030-91560-5_12(162-176)Online publication date: 1-Jan-2022
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