A Hybrid Probabilistic Multiobjective Evolutionary Algorithm for Commercial Recommendation Systems

Guoshuai Wei, Quanwang Wu, Mengchu Zhou

Research output: Contribution to journalArticlepeer-review

62 Scopus citations


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.

Original languageEnglish (US)
Article number9363322
Pages (from-to)589-598
Number of pages10
JournalIEEE Transactions on Computational Social Systems
Issue number3
StatePublished - Jun 2021

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Social Sciences (miscellaneous)
  • Human-Computer Interaction


  • Cold start
  • multiobjective evolutionary algorithm (MOEA)
  • profit
  • recommendation system (RS)


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