Cited By
View all- Tomita YYokoyama T(2024)Fair Reciprocal Recommendation in Matching MarketsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688130(209-218)Online publication date: 8-Oct-2024
Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets
Pages 12 - 23
Abstract
In matching markets such as job posting and online dating platforms, the recommender system plays a critical role in the success of the platform. Unlike standard recommender systems that suggest items to users, reciprocal recommender systems (RRSs) that suggest other users must take into account the mutual interests of users. In addition, ensuring that recommendation opportunities do not disproportionately favor popular users is essential for the total number of matches and for fairness among users. Existing recommendation methods in matching markets, however, face computational challenges on real-world scale platforms and depend on specific examination functions in the position-based model (PBM). In this paper, we introduce the reciprocal recommendation method based on the matching with transferable utility (TU matching) model in the context of ranking recommendations in matching markets, and propose a faster and examination-agnostic algorithm. Furthermore, we evaluate our approach on experiments with synthetic data and real-world data from an online dating platform in Japan. Our method performs better than or as well as existing methods in terms of the total number of matches and works well even in relatively large datasets for which one existing method does not work.
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Index Terms
- Fast and Examination-agnostic Reciprocal Recommendation in Matching Markets
Recommendations
Reciprocal Sequential Recommendation
RecSys '23: Proceedings of the 17th ACM Conference on Recommender SystemsReciprocal recommender system (RRS), considering a two-way matching between two parties, has been widely applied in online platforms like online dating and recruitment. Existing RRS models mainly capture static user preferences, which have neglected the ...
Fair Reciprocal Recommendation in Matching Markets
RecSys '24: Proceedings of the 18th ACM Conference on Recommender SystemsRecommender systems play an increasingly crucial role in shaping people’s opportunities, particularly in online dating platforms. It is essential from the user’s perspective to increase the probability of matching with a suitable partner while ensuring ...
Reciprocal Recommendation: Matching Users with the Right Users
SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information RetrievalReciprocal recommender systems, which recommend users to each other, have gained significant importance in various Internet services for connecting people in a personalized manner, such as: online dating, recruitment, socializing, learning, or skill-...
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September 2023
1406 pages
ISBN:9798400702419
DOI:10.1145/3604915
Copyright © 2023 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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- SIGIR: ACM Special Interest Group on Information Retrieval
- SIGCHI: ACM Special Interest Group on Computer-Human Interaction
- SIGecom: Special Interest Group on Economics and Computation
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Association for Computing Machinery
New York, NY, United States
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Published: 14 September 2023
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RecSys '23: Seventeenth ACM Conference on Recommender Systems
September 18 - 22, 2023
Singapore, Singapore
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Overall Acceptance Rate 254 of 1,295 submissions, 20%
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View all- Tomita YYokoyama T(2024)Fair Reciprocal Recommendation in Matching MarketsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688130(209-218)Online publication date: 8-Oct-2024
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