Abstract
In the era of information explosion, recommender systems have been widely used to reduce information load nowadays. However, mainly traditional recommendation techniques only paid attention on improving recommendation accuracy without considering additional criteria such as diversity, novelty. Moreover, such traditional recommendation algorithms were also struggled with matthew effect, that is, the gap between the popularity of popular and non-popular items grows. Therefore, a multi-objective recommendation model with extreme individual guided and mutation adaptation based on multi-objective evolutionary algorithms (MOEA-EIMA) is proposed in this paper. It maximizes two conflicting performance metrics termed as precision and novelty. In MOEA-EIMA, the iteration of population is guided by extreme individuals, and the adaptive mutation operator is designed for saving the better individuals. The algorithm is tested in several sparse datasets. The experiment results demonstrate the proposed algorithm can achieve a good trade-off between accuracy and novelty.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (Grant No. 61703256, 61806119), Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2022JM-381, 2017JQ6070) and the Fundamental Research Funds for the Central Universities (Program No. GK201803020, GK201603014).
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Cao, Y. et al. (2022). Personalized Recommendation Using Extreme Individual Guided and Adaptive Strategies. In: Shi, Z., Jin, Y., Zhang, X. (eds) Intelligence Science IV. ICIS 2022. IFIP Advances in Information and Communication Technology, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-14903-0_17
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