A Hybrid Probabilistic Multiobjective Evolutionary Algorithm for Commercial Recommendation Systems

Document Type

Article

Publication Date

6-1-2021

Abstract

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.

Identifier

85101806831 (Scopus)

Publication Title

IEEE Transactions on Computational Social Systems

External Full Text Location

https://doi.org/10.1109/TCSS.2021.3055823

e-ISSN

2329924X

First Page

589

Last Page

598

Issue

3

Volume

8

Grant

61672117

Fund Ref

National Natural Science Foundation of China

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