Abstract
As a technology based on statistics and knowledge discovery, recommendation system can automatically provide appropriate recommendations to users, which is considered as a very effective tool for reducing information load. The accuracy and diversity of recommendation are important objectives of evaluating an algorithm. In order to improve the diversity of recommendation, a personalized recommendation algorithm Multi-Objective Evolutionary Algorithm with Probabilistic-spreading and Genetic Mutation Adaptation (MOEA-PGMA) based on Personalized Recommendation based on Multi-Objective Evolutionary Optimization (MOEA-ProbS) is proposed in this paper. Low-grade and unpurchased items are preprocessed before predicting the scores to avoid recommending low-grade items to users and improve recommendation accuracy. By introducing adaptive mutation, the better individuals will survive in the evolution with a smaller mutation rate, and worse individuals will eliminate. The experimental results show that MOEA-PMGA has a higher population search ability compared to MOEA-ProbS, and has improved the accuracy and diversity on the optimal solution set.
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Acknowledgement
This work was partially supported by the National Natural Science Foundation of China (Grant Nos. 61673194, 61105128), Key Research and Development Program of Jiangsu Province, China (Grant No. BE2017630), the Postdoctoral Science Foundation of China (Grant No. 2014M560390), Six Talent Peaks Project of Jiangsu Province (Grant No. DZXX-025).
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Shi, X., Fang, W., Zhang, G. (2018). A Personalized Recommendation Algorithm Based on MOEA-ProbS. In: Tan, Y., Shi, Y., Tang, Q. (eds) Advances in Swarm Intelligence. ICSI 2018. Lecture Notes in Computer Science(), vol 10941. Springer, Cham. https://doi.org/10.1007/978-3-319-93815-8_54
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DOI: https://doi.org/10.1007/978-3-319-93815-8_54
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