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CC-MOEA: A Parallel Multi-objective Evolutionary Algorithm for Recommendation Systems

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Algorithms and Architectures for Parallel Processing (ICA3PP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12453))

Abstract

Recommender systems have nowadays been widely used in a variety of applications such as Amazon and Ebay. Traditional recommendation techniques mainly focus on recommendation accuracy only. In reality, other metrics such as diversity and novelty also play a key role for modern recommendation systems. Although some works based on multi-objective evolutionary algorithm have been proposed for multi-objective recommendation, they are usually very time-consuming because of the large data size of the RSs and the long-term evolution iterations and hence it greatly limits their application in practice. To address these shortcomings, this paper first designs a multi-objective recommendation system, taking into account diversity and novelty as well as accuracy. Then, a novel parallel multi-objective evolutionary algorithm called CC-MOEA is proposed to optimize these conflicting metrics. CC-MOEA is devised grounded on NSGA-II and a cooperative coevolutionary island model, and a parallel global non-dominated selection method is introduced to reduce the runtime of finding the global optimal individuals. Furthermore, a new initialization method and a crossover operator are specifically designed. The experimental results reveal that CC-MOEA outperforms some state-of-the-art algorithms in terms of hypervolume and runtime.

This work is supported in part by the National Natural Science Foundation of China under Grant 61702060 and 61672117, and the Fundamental Research Funds for the Central Universities of China under Grant 2019CDXYJSJ0021.

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Authors and Affiliations

  1. College of Computer Science, Chongqing University, Chongqing, China

    Guoshuai Wei & Quanwang Wu

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  1. Guoshuai Wei
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  2. Quanwang Wu
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Correspondence to Quanwang Wu .

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Editors and Affiliations

  1. Columbia University, New York, NY, USA

    Meikang Qiu

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Wei, G., Wu, Q. (2020). CC-MOEA: A Parallel Multi-objective Evolutionary Algorithm for Recommendation Systems. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12453. Springer, Cham. https://doi.org/10.1007/978-3-030-60239-0_45

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