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Key Opinion Leaders in Recommendation Systems: Opinion Elicitation and Diffusion

Authors:

James CaverleeAuthors Info & Claims

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

Pages 636 - 644

https://doi.org/10.1145/3336191.3371826

Published: 22 January 2020 Publication History

Abstract

Recommendation systems typically rely on the interactions between a crowd of ordinary users and items, ignoring the fact that many real-world communities are notably influenced by a small group of key opinion leaders, whose feedback on items wields outsize influence. With important positions in the community (e.g. have a large number of followers), their elite opinions are able to diffuse to the community and further impact what items we buy, what media we consume, and how we interact with online platforms. Hence, this paper investigates how to develop a novel recommendation system by explicitly capturing the influence from key opinion leaders to the whole community. Centering around opinion elicitation and diffusion, we propose an end-to-end Graph-based neural model - GoRec. Specifically, to preserve the multi-relations between key opinion leaders and items, GoRec elicits the opinions from key opinion leaders with a translation-based embedding method. Moreover, GoRec adopts the idea of Graph Neural Networks to model the elite opinion diffusion process for improved recommendation. Through experiments on Goodreads and Epinions, the proposed model outperforms state-of-the-art approaches by 10.75% and 9.28% on average in Top-K item recommendation.

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Index Terms

Key Opinion Leaders in Recommendation Systems: Opinion Elicitation and Diffusion
1. Human-centered computing
  1. Collaborative and social computing

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining

January 2020

950 pages

ISBN:9781450368223

DOI:10.1145/3336191

General Chairs:
James Caverlee
Texas A&M University
,
Xia "Ben" Hu
Texas A&M University
,
Program Chairs:
Mounia Lalmas
Spotify
,
Wei Wang
University of California, Los Angeles

Copyright © 2020 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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Published: 22 January 2020

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WSDM '20: The Thirteenth ACM International Conference on Web Search and Data Mining

February 3 - 7, 2020

TX, Houston, USA

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Cited By

Sun YSun ZDu YZhang JOng YBaeza-Yates RBonchi F(2024)Self-Supervised Denoising through Independent Cascade Graph Augmentation for Robust Social RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671958(2806-2817)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671958
Duan YTu YLu YWang X(2024)Improving graph collaborative filtering with view explorer for social recommendationJournal of Intelligent Information Systems10.1007/s10844-024-00865-wOnline publication date: 26-Jun-2024
https://doi.org/10.1007/s10844-024-00865-w
He WJin C(2024)A study on the influence of the characteristics of key opinion leaders on consumers’ purchase intention in live streaming commerce: based on dual-systems theoryElectronic Commerce Research10.1007/s10660-022-09651-824:2(1235-1265)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1007/s10660-022-09651-8
Maojie Z(2023)The impact of anchor characteristics on consumers’ willingness to pay a premium for food—an empirical studyFrontiers in Nutrition10.3389/fnut.2023.124050310Online publication date: 4-Sep-2023
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Tran KUehara T(2023)The influence of key opinion leaders on consumers' purchasing intention regarding green fashion productsFrontiers in Communication10.3389/fcomm.2023.12961748Online publication date: 1-Dec-2023
https://doi.org/10.3389/fcomm.2023.1296174
Jain LKatarya RSachdeva S(2023)Opinion Leaders for Information Diffusion Using Graph Neural Network in Online Social NetworksACM Transactions on the Web10.1145/358051617:2(1-37)Online publication date: 20-Jan-2023
https://dl.acm.org/doi/10.1145/3580516
Chaabane MRodriguez ISahnoun Y(2023)Systematic Literature Review of Social Media interactions2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)10.1109/ICECCME57830.2023.10252420(1-4)Online publication date: 19-Jul-2023
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Weng LZhang QLin ZWu LZhang J(2023)Integrating interactions between target users and opinion leaders for better recommendationsComputer Communications10.1016/j.comcom.2022.11.011198:C(98-107)Online publication date: 15-Jan-2023
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Chekkai NKheddouci H(2023)TOP-Key Influential Nodes for Opinion Leaders Identification in Travel Recommender SystemsAdvances in Model and Data Engineering in the Digitalization Era10.1007/978-3-031-23119-3_11(149-161)Online publication date: 10-Jan-2023
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Sun ZFang HYang JQu XLiu HYu DOng YZhang J(2022)DaisyRec 2.0: Benchmarking Recommendation for Rigorous EvaluationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2022.3231891(1-20)Online publication date: 2022
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