A recommendation algorithm for two-sided matching problems based on F-Score

According to the historical matched data and the agent's risk preference, the proposed algorithm calculates the agent's standardized F-Score to each agent on the opposite side. The agent on the opposite side which has the biggest standardized F-Score is recommended to the original agent. A typical application background of the algorithm is the parallel enrolment mechanism of college education in China. With contrast experiments between the proposed algorithm and other two recommendation algorithms using the real students and college admission data, it is found that the proposed algorithm has a high success recommendation rate and can respond to the agent's risk preference.

[1]  Alvin E. Roth,et al.  Two-Sided Matching: A Study in Game-Theoretic Modeling and Analysis , 1990 .

[2]  David A. Freedman,et al.  Machiavelli and the Gale-Shapley Algorithm , 1981 .

[3]  David Gale,et al.  The Two-Sided Matching Problem: Origin, Development and Current Issues , 2001, IGTR.

[4]  A. Roth.  The Evolution of the Labor Market for Medical Interns and Residents: A Case Study in Game Theory , 1984, Journal of Political Economy.

[5]  Nikolaos Polatidis,et al.  An evaluation of keyword, string similarity and very shallow syntactic matching for a university admissions processing infobot , 2013, Comput. Sci. Inf. Syst..

[6]  Simon Fong,et al.  Applying a hybrid model of neural network and decision tree classifier for predicting university admission , 2009, 2009 7th International Conference on Information, Communications and Signal Processing (ICICS).

[7]  Zhang Changbin.  The National University Entrance Examination and its influence on secondary school physics teaching in China , 1995 .

[8]  Olena Senyuta,et al.  Truncation in the Matching Markets and Market Inefficiency , 2014 .

[9]  Alexander Dekhtyar,et al.  Information Retrieval , 2018, Lecture Notes in Computer Science.

[10]  David Manlove,et al.  Two algorithms for the Student-Project Allocation problem , 2007, J. Discrete Algorithms.

[11]  Alvin E. Roth,et al.  COMMON AND CONFLICTING INTERESTS IN TWO-SIDED MATCHING MARKETS , 1985 .

[12]  L. S. Shapley,et al.  College Admissions and the Stability of Marriage , 2013, Am. Math. Mon..

[13]  Yan Chen,et al.  Chinese College Admissions and School Choice Reforms: Theory and Experiments , 2014 .

[14]  Mohd Shahizan Othman,et al.  Data Sets for Offline Evaluation of Scholar's Recommender System , 2013, ACIIDS.

[15]  Hiroyuki Adachi,et al.  A search model of two-sided matching under nontransferable utility , 2003, Journal of Economics Theory.

[16]  M. Rathinakumar,et al.  AN AUTOMATED IMPLEMENTATION OF INDIAN UNIVERSITY ADMISSION SYSTEM USING ARTIFICIAL NEURAL NETWORKS , 2014, SOCO 2014.

[17]  Guy Shani,et al.  Evaluating Recommendation Systems , 2011, Recommender Systems Handbook.

[18]  Abdul Hamid M. Ragab,et al.  Design and Implementation of a Hybrid Recommender System for Predicting College Admission , 2013 .

[19]  Simon Fong,et al.  An Automated University Admission Recommender System for Secondary School Students , .

[20]  Akihisa Tamura,et al.  A general two-sided matching market with discrete concave utility functions , 2006, Discret. Appl. Math..

[21]  Nasrollah Moghaddam Charkari,et al.  A recommender system for detection of leukemia based on cooperative game , 2009, 2009 17th Mediterranean Conference on Control and Automation.