tutorial

Recommendation for Multi-stakeholders and through Neural Review Mining

Authors:

Muthusamy Chelliah,

Sudeshna SarkarAuthors Info & Claims

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

Pages 2979 - 2981

https://doi.org/10.1145/3357384.3360321

Published: 03 November 2019 Publication History

Abstract

Recommender systems are able to produce a list of recommended items tailored to user preferences, while the end user is the only stakeholder in these traditional system. However, there could be multiple stakeholders in several applications domains (e.g., e-commerce, movies, music). Recommendations are necessary to be produced by balancing the needs of different stakeholders. First session of this tutorial introduces multi-stakeholder recommender systems (MSRS) with several case studies, and discusses the corresponding methods and challenges in MSRS. Reviews in an e-commerce platform may be mined to address cold-start problem and to generate explanations. Our earlier tutorial covered aspect-based sentiment analysis of products and topic models/distributed representations that bridge vocabulary gap between user reviews and product descriptions. Focus in the second session of this tutorial instead is on recent neural methods for review text mining - covering hands-on code for its use to enhance product recommendation. Each section will introduce topics from various mechanism (e.g., attention) and task (e.g., review ranking) perspectives, present cutting-edge research and a walk-through of programs executed on Jupyter notebook using real-world data sets.

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Cited By

Li SLi YZhao WDing BWen J(2021)Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count PredictionACM Transactions on Information Systems10.1145/346664040:1(1-29)Online publication date: 24-Nov-2021
https://dl.acm.org/doi/10.1145/3466640
Xia PJiang WWu JXiao SWang G(2021)Exploiting Temporal Dynamics in Product Reviews for Dynamic Sentiment Prediction at the Aspect LevelACM Transactions on Knowledge Discovery from Data10.1145/344145115:4(1-29)Online publication date: 18-Apr-2021
https://dl.acm.org/doi/10.1145/3441451
Guíñez FRuiz JSánchez M(2021)Quantification of the Impact of Popularity Bias in Multi-stakeholder and Time-Aware EnvironmentsAdvances in Bias and Fairness in Information Retrieval10.1007/978-3-030-78818-6_8(78-91)Online publication date: 25-Jun-2021
https://doi.org/10.1007/978-3-030-78818-6_8
Show More Cited By

Recommendation for Multi-stakeholders and through Neural Review Mining
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals

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Published In

cover image ACM Conferences

CIKM '19: Proceedings of the 28th ACM International Conference on Information and Knowledge Management

November 2019

3373 pages

ISBN:9781450369763

DOI:10.1145/3357384

General Chairs:
Wenwu Zhu
Tsinghua University, China
,
Dacheng Tao
University of Massachusetts, USA
,
Xueqi Cheng
Institute of Computing Technology, CAS, China
,
Program Chairs:
Peng Cui
Tsinghua University, China
,
Elke Rundensteiner
Worcester Polytechnic Institute, USA
,
David Carmel
Amazon Research, USA
,
Qi He
LinkedIn, USA
,
Jeffrey Xu Yu
Chinese University of Hong Kong, China

Copyright © 2019 Owner/Author.

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

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Publication History

Published: 03 November 2019

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CIKM '19

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CIKM '19: The 28th ACM International Conference on Information and Knowledge Management

November 3 - 7, 2019

Beijing, China

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CIKM '19 Paper Acceptance Rate 202 of 1,031 submissions, 20%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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CIKM '25

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The 34th ACM International Conference on Information and Knowledge Management

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Cited By

Li SLi YZhao WDing BWen J(2021)Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count PredictionACM Transactions on Information Systems10.1145/346664040:1(1-29)Online publication date: 24-Nov-2021
https://dl.acm.org/doi/10.1145/3466640
Xia PJiang WWu JXiao SWang G(2021)Exploiting Temporal Dynamics in Product Reviews for Dynamic Sentiment Prediction at the Aspect LevelACM Transactions on Knowledge Discovery from Data10.1145/344145115:4(1-29)Online publication date: 18-Apr-2021
https://dl.acm.org/doi/10.1145/3441451
Guíñez FRuiz JSánchez M(2021)Quantification of the Impact of Popularity Bias in Multi-stakeholder and Time-Aware EnvironmentsAdvances in Bias and Fairness in Information Retrieval10.1007/978-3-030-78818-6_8(78-91)Online publication date: 25-Jun-2021
https://doi.org/10.1007/978-3-030-78818-6_8
Zheng Y(2019)Preference corrections: capturing student and instructor perceptions in educational recommendationsSmart Learning Environments10.1186/s40561-019-0092-36:1Online publication date: 27-Dec-2019
https://doi.org/10.1186/s40561-019-0092-3

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