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RECON

Luiz Pizzato, Tomek Rej, Thomas Chung, Irena Koprinska, Judy Kay
2010 Proceedings of the fourth ACM conference on Recommender systems - RecSys '10  
We also found reciprocity to help with the cold start problem obtaining a success rate of 26% for the top ten recommendations for new users.  ...  We investigated the predictive power gained by taking account of reciprocity, finding that it is substantial, for example it improved the success rate of the top ten recommendations from 26% to 45% and  ...  This supports our hypothesis that reciprocity is important for recommender systems that involve people in both sides of the recommendations (i.e. reciprocal recommenders).  ... 
doi:10.1145/1864708.1864747 dblp:conf/recsys/PizzatoRCKK10 fatcat:6f4s7bd4ebc7rf67wlxwwa6pgu

Recommending people to people: the nature of reciprocal recommenders with a case study in online dating

Luiz Pizzato, Tomasz Rej, Joshua Akehurst, Irena Koprinska, Kalina Yacef, Judy Kay
2012 User modeling and user-adapted interaction  
People-to-people recommenders constitute an important class of recommender systems.  ...  Examples include online dating, where people have the common goal of finding a partner, and employment websites where one group of users needs to find a job (employer) and another group needs to find an  ...  In Table 2 , Row [e], we observed that in reciprocal recommenders, many users leave the system after a successful recommendation.  ... 
doi:10.1007/s11257-012-9125-0 fatcat:65qyrgwa3rfd5n7gk2va66qo6y

Finding Someone You Will Like and Who Won't Reject You [chapter]

Luiz Augusto Pizzato, Tomek Rej, Kalina Yacef, Irena Koprinska, Judy Kay
2011 Lecture Notes in Computer Science  
This paper explores ways to address the problem of the high cost problem of poor recommendations in reciprocal recommender systems.  ...  These systems recommend one person to another and require that both people like each other for the recommendation to be successful.  ...  This might mean that, compared with conventional recommenders, the reciprocal recommender may need to find recommendations that may not be a particularly good match to the user's preference model; in this  ... 
doi:10.1007/978-3-642-22362-4_23 fatcat:6elat5phxzcijkxtm6nebmo3wu

Explicit and Implicit User Preferences in Online Dating [chapter]

Joshua Akehurst, Irena Koprinska, Kalina Yacef, Luiz Pizzato, Judy Kay, Tomasz Rej
2012 Lecture Notes in Computer Science  
We also propose an approach that uses the explicit and implicit preferences to rank the candidates in our recommender system.  ...  In this paper we study user behavior in online dating, in particular the differences between the implicit and explicit user preferences.  ...  This work is supported by the Smart Services Cooperative Research Centre.  ... 
doi:10.1007/978-3-642-28320-8_2 fatcat:vzgecyqslfdcvi3paqmvm7t2tq

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

MEET

Lei Li, Tao Li
2012 Proceedings of the 21st ACM international conference on Information and knowledge management - CIKM '12  
Reciprocal recommender systems refer to systems from which users can obtain recommendations of other individuals by satisfying preferences of both parties being involved.  ...  In this paper, we propose MEETa generalized fraMework for rEciprocal rEcommendaTion, in which we model the correlations of users as a bipartite graph that maintains both local and global "reciprocal" utilities  ...  ACKNOWLEDGEMENT The work is partially supported by NSF grants DMS-0915110 and CNS-1126619 and DHS grants 2009-ST-062-000016 and 2010-ST-062-000039.  ... 
doi:10.1145/2396761.2396770 dblp:conf/cikm/LiL12 fatcat:p3xxvosdrzdw5io7ifryytbp64

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
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.  ...  Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation.  ...  They want to have a profitable business by having repeated users, even though the best user experience would be for each user to instantly find a match (a successful date/partner) and never return.  ... 
arXiv:1905.01986v2 fatcat:6a4wfm6mwfhzfghdgvcsifdgbq

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.  ...  Similar ideas have appeared in group recommender systems where the goal is to find recommendation(s) that can maximize the utility of all users in the group [22] .  ... 
arXiv:1707.09258v1 fatcat:up2vg7dxqrflvl5rayovq6qpiq

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  
We find that our method is significantly more effective in increasing the number of successful interactions compared to a state-of-the-art recommendation method.  ...  Many of these platforms include recommender systems which aim at helping users discover other people who will also be interested in them.  ...  INTRODUCTION Reciprocal recommender systems (RRS) recommend people to people [19] , as opposed to traditional recommender systems which recommend items to people.  ... 
doi:10.1145/3240323.3240349 dblp:conf/recsys/KleinermanR0K18 fatcat:hadpzsntwjeyrby7wsgqykagna

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.  ...  Online recruiting systems have gained immense attention in the wake of more and more job seekers searching jobs and enterprises finding candidates on the Internet.  ...  Given a target user u and a search result list Q u, we are interested in finding a relevant user v ∈ Q u, such that (u,v) is a successful match.  ... 
doi:10.1145/2487575.2488199 dblp:conf/kdd/HongLLP13 fatcat:ui6nc7lh65hfdkwbuyz4hcgcii

ImRec: Learning Reciprocal Preferences Using Images

James Neve, Ryan McConville
2020 Fourteenth ACM Conference on Recommender Systems  
Reciprocal Recommender Systems are recommender systems for social platforms that connect people to people. They are commonly used in online dating, social networks and recruitment services.  ...  In this study, we present a novel method of making reciprocal recommendations based on image data.  ...  Yusuke Usui (AI Team Leader) at Eureka Inc. for their support.  ... 
doi:10.1145/3383313.3411476 dblp:conf/recsys/NeveM20 fatcat:vjpbvb2cnngyzgnachk4ib6uvi

Photos Are All You Need for Reciprocal Recommendation in Online Dating [article]

James Neve, Ryan McConville
2021 arXiv   pre-print
Reciprocal Recommenders are a subset of recommender systems, where the items in question are people, and the objective is therefore to predict a bidirectional preference relation.  ...  In particular, images provided by users are a crucial part of user preference, and one that is not exploited much in the literature.  ...  Reciprocal Recommender Systems RRSs are recommender systems used for person-to-person matching, in settings such as online dating, social networks [6] and job recommendation [22] .  ... 
arXiv:2108.11714v1 fatcat:7wiyml3uzvhepcxpt7lo2lpz4y

A survey of job recommender systems

Shaha T. Al-Otaibi
2012 International Journal of Physical Sciences  
The recommender system technology aims to help users in finding items that match their personnel interests; it has a successful usage in e-commerce applications to deal with problems related to information  ...  In order to improve the e-recruiting functionality, many recommender system approaches have been proposed.  ...  This model can be benefited from successful recommender systems techniques that applied in e-commerce applications and produced good recommendations to users.  ... 
doi:10.5897/ijps12.482 fatcat:rg4pm4zzgrhhbogdxsfswvglam

Extracting Users' Explicit Preferences from Free-text using Second Order Co-occurrence PMI in Indian Matrimony

Nazia Tabassum, Tanvir Ahmad
2020 Procedia Computer Science  
The methodology explained in this paper automatically prioritizes these features which can be used to design a Weighted Reciprocal Recommendation model to generate more efficient recommendations.  ...  This paper is a corpus-based method for extracting users' explicit preferences from free-text part of the registered user profile in Indian matchmaking system using Second order Co-occurrence PMI (SOC-PMI  ...  Traditional / Classical Recommender System Reciprocal Recommender System Item-to-person recommendation People-to-people recommendation Success is determined by only one side (user -side) who is  ... 
doi:10.1016/j.procs.2020.03.245 fatcat:keqb3krufffxjbwefu2bs4aplu

User-to-User Recommendation using the Concept of Movement Patterns: A Study using a Dating Social Network

Mohammed Al-Zeyadi, Frans Coenen, Alexei Lisitsa
2017 Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management  
The provision of effective User-to-User recommendation systems have thus become an essential part of successful dating networks.  ...  The idea has been built into a User-to-User recommender system, the RecoMP system.  ...  thank the China University of Science and Technology, and the School of Statistics at the Renmin University of China Statistical Centre, for providing the jiayuan.com dataset used for evaluation purposes in  ... 
doi:10.5220/0006494601730180 dblp:conf/ic3k/Al-ZeyadiCL17 fatcat:z22uivf3gbcq5ixzu5s4wkmane
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