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Optimally balancing receiver and recommended users' importance in reciprocal recommender systems

Akiva Kleinerman, Ariel Rosenfeld, Francesco Ricci, Sarit Kraus
2018 Proceedings of the 12th ACM Conference on Recommender Systems - RecSys '18  
For each user receiving a recommendation, our method finds the optimal balance of two criteria: a) the likelihood of the user accepting the recommendation; and b) the likelihood of the recommended user  ...  These recommender systems benefit from contemplating the interest of both sides of the recommended match, however the question of how to optimally balance the interest and the response of both sides remains  ...  Optimally Balancing Receiver and Recommended Users' Importance In Algorithm 2 we give the general scheme for our recommendation algorithm, where ServiceU ser Features (row 10) is a function which obtains  ... 
doi:10.1145/3240323.3240349 dblp:conf/recsys/KleinermanR0K18 fatcat:hadpzsntwjeyrby7wsgqykagna

Beyond Personalization: Research Directions in Multistakeholder Recommendation [article]

Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, Luiz Pizzato
2019 arXiv   pre-print
Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation.  ...  Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes.  ...  (the user receiving the recommendation and the user being recommended).  ... 
arXiv:1905.01986v2 fatcat:6a4wfm6mwfhzfghdgvcsifdgbq

Recommender Systems as Multistakeholder Environments

Himan Abdollahpouri, Robin Burke, Bamshad Mobasher
2017 Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization - UMAP '17  
For example, fairness and balance across stakeholders is important in some recommendation applications; achieving a goal such as promoting new sellers in a marketplace might be important in others.  ...  In research practice, recommender systems are typically evaluated on their ability to provide items that satisfy the needs and interests of the end user.  ...  Fairness and balance are important examples of system-level objectives, and these social-welfare-oriented goals may at times run counter to individual preferences.  ... 
doi:10.1145/3079628.3079657 dblp:conf/um/AbdollahpouriBM17 fatcat:4fxewwxu6fcbrnauhnxh5pkysq

Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities towards Social Recommendation [article]

Ivan Palomares, Carlos Porcel, Luiz Pizzato, Ido Guy, Enrique Herrera-Viedma
2021 arXiv   pre-print
, in a Reciprocal Recommender System (RRS) users become the item being recommended to other users.  ...  Recommender systems arose as a data-driven personalized decision support tool to assist users in these situations: they are able to process user-related data, filtering and recommending items based on  ...  Acknowledgement We appreciate the financial support of Spanish State Research Agency -Ministry of Science and Innovation Grant PID2019-103880RB-I00.  ... 
arXiv:2007.16120v3 fatcat:ly6d45grijbpjbk7ubm4hj3yyq

Stochastic matching and collaborative filtering to recommend people to people

Luiz Augusto Pizzato, Cameron Silvestrini
2011 Proceedings of the fifth ACM conference on Recommender systems - RecSys '11  
In this paper, we use collaborative filtering to generate recommendations for all users, and by using stochastic matching we select a number of reciprocal recommendations for each user that maximizes the  ...  In this way, all users, regardless of their popularity, will receive the same number of recommendations the number of times they will be recommended to others.  ...  When looking into the popularity spectrum and people recommender systems, there is a right balance between the number of recommendations that each user should receive and the number of times they should  ... 
doi:10.1145/2043932.2043998 dblp:conf/recsys/PizzatoS11 fatcat:vsot2lwlejajtmob6u4ygdy5iq

Patterns of Multistakeholder Recommendation [article]

Robin Burke, Himan Abdollahpouri
2017 arXiv   pre-print
However, in many settings, the end-user of the recommendations is not the only party whose needs must be represented in recommendation generation.  ...  Recommender systems are personalized information systems.  ...  Here there is a form of reciprocation in that the ad should be appealing to the user and the user should be in the defined target audience.  ... 
arXiv:1707.09258v1 fatcat:up2vg7dxqrflvl5rayovq6qpiq

Multi-Stakeholder Recommendation: Applications and Challenges [article]

Yong Zheng
2017 arXiv   pre-print
Traditional recommender systems only focus on optimizing the utility of the end users who are the receiver of the recommendations.  ...  Recommender systems have been successfully applied to assist decision making by producing a list of item recommendations tailored to user preferences.  ...  Reciprocal Recommendation In a reciprocal recommender, the user and the item have similar standing in the system, in that both have preferences that must be satis ed [14] .  ... 
arXiv:1707.08913v1 fatcat:uv4tadr4dfddfaui4snnj6ioqi

Image Recommendation With Reciprocal Social Influence

Yuan Meng, Chunyan Han, Yongfeng Zhang, Yanjie Li, Guibing Guo
2019 IEEE Access  
In this paper, we propose a deep neural network for image recommendation (dubbed RSIM) by leveraging reciprocal social influence, and optimize the preferences of users and friends simultaneously.  ...  Image recommendation plays an important role for exploring user potential interests in largescale image sharing websites (e.g., Flickr and Instagram).  ...  RELATED WORK In this section, we provide a brief overview about related research in image recommendation and social recommender systems. A.  ... 
doi:10.1109/access.2019.2939403 fatcat:loklgbbntnexphzgmwfpnv5ave

Personalizing Fairness-aware Re-ranking [article]

Weiwen Liu, Robin Burke
2018 arXiv   pre-print
Personalized recommendation brings about novel challenges in ensuring fairness, especially in scenarios in which users are not the only stakeholders involved in the recommender system.  ...  For example, the system may want to ensure that items from different providers have a fair chance of being recommended.  ...  The system receives items from providers and recommends them to the consumers.  ... 
arXiv:1809.02921v2 fatcat:srj2no4jwndj3jhwu7w2en47am

Multisided Fairness for Recommendation [article]

Robin Burke
2017 arXiv   pre-print
Based on these considerations, we present a taxonomy of classes of fairness-aware recommender systems and suggest possible fairness-aware recommendation architectures.  ...  In this paper, we extend the concept of fairness to recommendation.  ...  It is also important to note that recommender systems exist to facilitate transactions.  ... 
arXiv:1707.00093v2 fatcat:a3kwumbuqrgfzldvvetywea2k4

Behaviorism is Not Enough

Michael D. Ekstrand, Martijn C. Willemsen
2016 Proceedings of the 10th ACM Conference on Recommender Systems - RecSys '16  
not just observing what they do will enable important developments in the future of recommender systems.  ...  In this paper, we argue that listening to what users say -about the items and recommendations they like, the control they wish to exert on the output, and the ways in which they perceive the system -and  ...  ACKNOWLEDGEMENTS We thank Jennifer Ekstrand for her valuable insights and feedback on draft versions of this paper.  ... 
doi:10.1145/2959100.2959179 dblp:conf/recsys/EkstrandW16 fatcat:6vxx23nfm5abbpi5lsewygiali

FAIR: Fairness-Aware Information Retrieval Evaluation [article]

Ruoyuan Gao, Yingqiang Ge, Chirag Shah
2021 arXiv   pre-print
Based on this metric, we developed an effective ranking algorithm that jointly optimized user utility and fairness.  ...  With the emerging needs of creating fairness-aware solutions for search and recommendation systems, a daunting challenge exists of evaluating such solutions.  ...  ., 2019; Gao & Shah, 2020) in search and recommendations. In this algorithm, we utilize our proposed FAIR metric to customize the balance between fairness and the fairness-aware system utility.  ... 
arXiv:2106.08527v1 fatcat:iuj3f3xq6zd7llodxxhvf37lke

Multiple Stakeholders in Music Recommender Systems [article]

Himan Abdollahpouri, Steve Essinger
2017 arXiv   pre-print
Users can typically listen to a variety of songs tailored to their personal tastes and preferences. Music is not the only type of content encountered in these services, however.  ...  These stakeholders each have their own objectives and must work in concert to sustain a healthy music recommendation service.  ...  For example, in reciprocal recommendation, a successful recommendation is not the one that is only acceptable by the receiver of the recommendation.  ... 
arXiv:1708.00120v1 fatcat:64irqhofpbffdmlcixsdb65ysm

iHR

Wenxing Hong, Lei Li, Tao Li, Wenfu Pan
2013 Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '13  
The system utilizes the latest advances in data mining and recommendation technologies to create a user-oriented service for a myriad of audience in job marketing community.  ...  A critical problem in a recruiting system is how to maximally satisfy the desires of both job seekers and enterprises with reasonable recommendations or search results.  ...  ; users can also receive recommendations from the system, and then choose some of them to view.  ... 
doi:10.1145/2487575.2488199 dblp:conf/kdd/HongLLP13 fatcat:ui6nc7lh65hfdkwbuyz4hcgcii

Regulation Mechanisms in an Open Social Media Using a Contact Recommender System [chapter]

L. Vignollet, M. Plu, J. C. Marty, L. Agosto
2005 Communities and Technologies 2005  
This recommender system selects the recommended relationships in such a way it should optimize some global qualities of the social media.  ...  Second we have introduced a contact recommender system to help users to carefully open their closed relationship network.  ...  We also want to thank Pascal Bellec for his work in developing the system.  ... 
doi:10.1007/1-4020-3591-8_22 fatcat:7btfl3av6jaefgr2vnv36dyqcu
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