research-article

Hybrid Reciprocal Recommender Systems: Integrating Item-to-User Principles in Reciprocal Recommendation

Authors:

Iván PalomaresAuthors Info & Claims

WWW '20: Companion Proceedings of the Web Conference 2020

Pages 848 - 853

https://doi.org/10.1145/3366424.3383295

Published: 23 April 2020 Publication History

Abstract

Reciprocal Recommender Systems (RRS) recommend users to other users in a personalised manner, in scenarios where both sides of the preference relation must be considered. Existing RRS approaches based on collaborative filtering or content-based filtering, have been used for enhancing user experience in online dating and other online services aimed at connecting users with each other. However, some of these services e.g. skill sharing platforms, are still pervaded by content published, shared and consumed by users, consequently there is a valuable source of item-to-user preferential information not captured by existing RRS models. We present a novel hybrid RRS framework that integrates user preferences towards content in reciprocal recommendation, and we instantiate and evaluate it using data from Cookpad, a recipe sharing social media platform. As part of our model, we also implement a novel content-based extension of Jaccard similarity measure that operates on word embeddings.

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Cited By

Nagina RKaur JBhalla P(2024)Preserving Traditional Recipes and Methods in the Culinary WorldGlobal Sustainable Practices in Gastronomic Tourism10.4018/979-8-3693-7096-4.ch024(403-428)Online publication date: 8-Nov-2024
https://doi.org/10.4018/979-8-3693-7096-4.ch024
Kumar CChowdary CMeena A(2024)Recent trends in recommender systems: a surveyInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00349-113:4Online publication date: 10-Oct-2024
https://doi.org/10.1007/s13735-024-00349-1
Verma SHarit SMunjal K(2024)SRRS: Design and Development of a Scholarly Reciprocal Recommendation SystemScientometrics10.1007/s11192-024-05143-8129:11(6839-6866)Online publication date: 21-Sep-2024
https://doi.org/10.1007/s11192-024-05143-8
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Index Terms

Hybrid Reciprocal Recommender Systems: Integrating Item-to-User Principles in Reciprocal Recommendation
1. Human-centered computing
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
  2. Information systems applications

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

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Published In

cover image ACM Conferences

WWW '20: Companion Proceedings of the Web Conference 2020

April 2020

854 pages

ISBN:9781450370240

DOI:10.1145/3366424

Editors:
Amal El Fallah Seghrouchni
Sorbonne University, France
,
Gita Sukthankar
University of Central Florida, United States
,
Tie-Yan Liu
Microsoft Research Asia, China
,
Maarten van Steen
University of Twente, Netherlands

Copyright © 2020 ACM.

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Published: 23 April 2020

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WWW '20: The Web Conference 2020

April 20 - 24, 2020

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Cited By

Nagina RKaur JBhalla P(2024)Preserving Traditional Recipes and Methods in the Culinary WorldGlobal Sustainable Practices in Gastronomic Tourism10.4018/979-8-3693-7096-4.ch024(403-428)Online publication date: 8-Nov-2024
https://doi.org/10.4018/979-8-3693-7096-4.ch024
Kumar CChowdary CMeena A(2024)Recent trends in recommender systems: a surveyInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00349-113:4Online publication date: 10-Oct-2024
https://doi.org/10.1007/s13735-024-00349-1
Verma SHarit SMunjal K(2024)SRRS: Design and Development of a Scholarly Reciprocal Recommendation SystemScientometrics10.1007/s11192-024-05143-8129:11(6839-6866)Online publication date: 21-Sep-2024
https://doi.org/10.1007/s11192-024-05143-8
Kumari TSharma RGupta BBedi P(2023)Generating Popularity-Aware Reciprocal Recommendations Using Siamese Bi-Directional Gated Recurrent Units NetworkVietnam Journal of Computer Science10.1142/S219688882350004510:03(273-301)Online publication date: 31-May-2023
https://doi.org/10.1142/S2196888823500045
Kumari TGupta BSharma RBedi P(2023)Empowering reciprocal recommender system using contextual bandits and argumentation based explanationsWorld Wide Web10.1007/s11280-023-01173-z26:5(2969-3000)Online publication date: 29-May-2023
https://doi.org/10.1007/s11280-023-01173-z
Chang YShu LDu EChen CZhang ZZheng ZHuang YXing X(2022)GraphRR: A multiplex Graph based Reciprocal friend Recommender system with applications on online gaming serviceKnowledge-Based Systems10.1016/j.knosys.2022.109187251(109187)Online publication date: Sep-2022
https://doi.org/10.1016/j.knosys.2022.109187
Kumari TSharma RBedi P(2022)A contextual-bandit approach for multifaceted reciprocal recommendations in online datingJournal of Intelligent Information Systems10.1007/s10844-022-00708-659:3(705-731)Online publication date: 25-Jun-2022
https://doi.org/10.1007/s10844-022-00708-6
Zhu YZhao HZhang WYe GChen HZhang NChen HShen HZhuang YSmith JYang YCesar PMetze FPrabhakaran B(2021)Knowledge Perceived Multi-modal Pretraining in E-commerceProceedings of the 29th ACM International Conference on Multimedia10.1145/3474085.3475648(2744-2752)Online publication date: 17-Oct-2021
https://dl.acm.org/doi/10.1145/3474085.3475648
Kumari TSharma RBedi P(2021)Multifaceted Reciprocal Recommendations for Online Dating2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO51393.2021.9596224(1-6)Online publication date: 3-Sep-2021
https://doi.org/10.1109/ICRITO51393.2021.9596224
Pradhan RVarshney JGoyal KKumari L(2021)Job Recommendation System Using Content and Collaborative-Based FilteringInternational Conference on Innovative Computing and Communications10.1007/978-981-16-2594-7_47(575-583)Online publication date: 18-Aug-2021
https://doi.org/10.1007/978-981-16-2594-7_47
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