A Hybrid Probabilistic Multiobjective Evolutionary Algorithm for Commercial Recommendation Systems

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 i...

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Veröffentlicht in:IEEE transactions on computational social systems Jg. 8; H. 3; S. 589 - 598
Hauptverfasser: Wei, Guoshuai, Wu, Quanwang, Zhou, Mengchu
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 01.06.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Cold start
Complex systems
Evolutionary algorithms
Evolutionary computation
Genetic algorithms
Genetics
Linear programming
Measurement
multiobjective evolutionary algorithm (MOEA)
Optimization
Pareto optimization
Probabilistic logic
profit
recommendation system (RS)
Recommender systems
ISSN:2329-924X, 2373-7476
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:2329-924X
2373-7476
DOI:10.1109/TCSS.2021.3055823