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Implicit social recommendation algorithm based on multilayer fuzzy perception similarity

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Abstract

Most recommender systems are essentially a contextually accurate matching between users and items with similarities. Thus, similarity is particularly important to the recommendation process. Furthermore, the highest goal of similarity is to simulate the subjective human feeling of similarity, i.e., to simulate objective feature engineering in a way that is as consistent with subjective feeling as possible. By studying the subjective cognition of similarity, we found that the process could be divided into two stages, namely, perception and comprehension. Perception has fuzziness in that deterministic data cannot accurately describe subjective perception and judge emotional tendencies. Second, comprehension has gradations such that a linear model easily underfits the similarity. To address these two problems, we proposed a new implicit social recommendation algorithm based on multilayer fuzzy perception similarity. An extensive experimental study conducted on benchmark datasets showed that the proposed algorithm is very competitive with some of the traditional recommendation algorithms and state-of-the-art neural network algorithms, especially in terms of the obtained rankings.

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Acknowledgements

This work was supported by the Educational Commission Key Program of GuangDong Province of China under Grants 2020ZDZX3066 and by the National Natural Science Foundation of China under Grants 11991023, 61877049.

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Authors and Affiliations

  1. Guangdong University of Finance, Guangdong, China

    Di Han

  2. Xi’an Aeronautical University, Xi’an, China

    Yijun Chen

  3. Xi’an Jiaotong University, Xi’an, China

    Shuya Zhang

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  1. Di Han
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  2. Yijun Chen
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  3. Shuya Zhang
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Correspondence to Di Han.

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Han, D., Chen, Y. & Zhang, S. Implicit social recommendation algorithm based on multilayer fuzzy perception similarity. Int. J. Mach. Learn. & Cyber. 13, 357–369 (2022). https://doi.org/10.1007/s13042-021-01409-2

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