TY - GEN
T1 - Search based recommender system using many-objective evolutionary algorithm
AU - LI, Bingdong
AU - QIAN, Chao
AU - LI, Jinlong
AU - TANG, Ke
AU - YAO, Xin
N1 - This work was supported in part by the National Natural Science Foundation of China (Grant No. 61329302 and Grant No. 61573328), the Program for New Century Excellent Talents in University (Grant No. NCET-12-0512) and EPSRC (Grant No. EP/K001523/1) and the Natural Science Key Research Project for Higher Education Institutions of Anhui Province (KJ2016A438) and the Fundamental Research Funds for the Central Universities (WK2150110002). Xin Yao was also supported by a Royal Society Wolfson Research Merit Award. The authors would like to thank Prof. Maoguo Gong from Xidian University, Xi'an, China for kindly sharing the sourcecode with us.
PY - 2016/7
Y1 - 2016/7
N2 - With the explosively increase of information and products, recommender systems have played a more and more important role in the recent years. Various recommendation algorithms, such as content-based methods and collaborative filtering methods, have been proposed. There are a number of performance metrics for evaluating recommender systems, and considering only the precision or diversity might be inappropriate. However, to the best of our knowledge, no existing work has considered recommendation with many objectives. In this paper, we model a many-objective search-based recommender system and adopt a recently proposed many-objective evolutionary algorithm to optimize it. Experimental results on the Movielens data set demonstrate that our algorithm performs better in terms of Generational Distance (GD), Inverted Generational Distance (IGD) and Hypervolume (HV) on most test cases. © 2016 IEEE.
AB - With the explosively increase of information and products, recommender systems have played a more and more important role in the recent years. Various recommendation algorithms, such as content-based methods and collaborative filtering methods, have been proposed. There are a number of performance metrics for evaluating recommender systems, and considering only the precision or diversity might be inappropriate. However, to the best of our knowledge, no existing work has considered recommendation with many objectives. In this paper, we model a many-objective search-based recommender system and adopt a recently proposed many-objective evolutionary algorithm to optimize it. Experimental results on the Movielens data set demonstrate that our algorithm performs better in terms of Generational Distance (GD), Inverted Generational Distance (IGD) and Hypervolume (HV) on most test cases. © 2016 IEEE.
UR - http://www.scopus.com/inward/record.url?scp=85008264685&partnerID=8YFLogxK
U2 - 10.1109/CEC.2016.7743786
DO - 10.1109/CEC.2016.7743786
M3 - Conference paper (refereed)
SN - 9781509006229
SP - 120
EP - 126
BT - 2016 IEEE Congress on Evolutionary Computation, CEC 2016
PB - Institute of Electrical and Electronics Engineers Inc.
ER -