TY - JOUR
T1 - A Hybrid Probabilistic Multiobjective Evolutionary Algorithm for Commercial Recommendation Systems
AU - Wei, Guoshuai
AU - Wu, Quanwang
AU - Zhou, Mengchu
N1 - Funding Information:
Manuscript received September 7, 2020; revised December 26, 2020; accepted January 25, 2021. Date of publication February 25, 2021; date of current version May 28, 2021. This work was supported in part by the National Natural Science Foundation of China under Grant 61702060 and Grant 61672117 and in part by the Deanship of Scientific Research (DSR) at King Abdulaziz University under Grant D-503-135-1441. (Corresponding author: Quanwang Wu.) Guoshuai Wei and Quanwang Wu are with the College of Computer Science, Chongqing University, Chongqing 400044, China (e-mail: wgs0208@foxmail.com; wqw@cqu.edu.cn).
Publisher Copyright:
© 2014 IEEE.
PY - 2021/6
Y1 - 2021/6
N2 - As big-data-driven complex systems, commercial recommendation systems (RSs) have been widely used in such companies as Amazon and Ebay. Their core aim is to maximize total profit, which relies on recommendation accuracy and profits from recommended items. It is also important for them to treat new items equally for a long-term run. However, traditional recommendation techniques mainly focus on recommendation accuracy and suffer from a cold-start problem (i.e., new items cannot be recommended). Differing from them, this work designs a multiobjective RS by considering item profit and novelty besides accuracy. Then, a hybrid probabilistic multiobjective evolutionary algorithm (MOEA) is proposed to optimize these conflicting metrics. In it, some specifically designed genetic operators are proposed, and two classical MOEA frameworks are adaptively combined such that it owns their complementary advantages. The experimental results reveal that it outperforms some state-of-the-art algorithms as it achieves a higher hypervolume value than them.
AB - As big-data-driven complex systems, commercial recommendation systems (RSs) have been widely used in such companies as Amazon and Ebay. Their core aim is to maximize total profit, which relies on recommendation accuracy and profits from recommended items. It is also important for them to treat new items equally for a long-term run. However, traditional recommendation techniques mainly focus on recommendation accuracy and suffer from a cold-start problem (i.e., new items cannot be recommended). Differing from them, this work designs a multiobjective RS by considering item profit and novelty besides accuracy. Then, a hybrid probabilistic multiobjective evolutionary algorithm (MOEA) is proposed to optimize these conflicting metrics. In it, some specifically designed genetic operators are proposed, and two classical MOEA frameworks are adaptively combined such that it owns their complementary advantages. The experimental results reveal that it outperforms some state-of-the-art algorithms as it achieves a higher hypervolume value than them.
KW - Cold start
KW - multiobjective evolutionary algorithm (MOEA)
KW - profit
KW - recommendation system (RS)
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U2 - 10.1109/TCSS.2021.3055823
DO - 10.1109/TCSS.2021.3055823
M3 - Article
AN - SCOPUS:85101806831
SN - 2329-924X
VL - 8
SP - 589
EP - 598
JO - IEEE Transactions on Computational Social Systems
JF - IEEE Transactions on Computational Social Systems
IS - 3
M1 - 9363322
ER -