Abstract
Smart applications are appealing an accurate matching between users and items, in which recommendation technologies are applied widely. Since recommendation serve for two roles, namely users and items, accuracy is not the only focus, the diversification and fairness should also be paid more attention for improving recommendation performance. The tradeoff among the accuracy, diversification and fairness on recommendation is bringing a big challenge. This paper proposed a novelty recommendation model to ensure the recommendation performance, which introduces a multi-variate linear regression model to cooperate with the collaborative filtering method. This study utilizes an improved similarity metrics to discover the closeness between users and item categories under the help of the collaborative filtering methods, and exploits the micro attribute information of items by a multi-variate linear regression model to decide the final recommended items. The experimental results show that our proposed method can provide better recommendation accuracy, diversification and fairness than the recommendation based on pure collaborative filtering method.
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Acknowledgments
This study is funded by the National Natural Science Foundation of China (No. 61862013, 61462017, U1501252, U1711263, 61662015), Guangxi Natural Science Foundation of China (No. 2018GXNSFAA281199, 2017GXNSFAA198035), Guangxi Key Laboratory of Automatic Measurement Technology and Instrument (No.YQ19109) and Guangxi Key Laboratory of Trusted Software (No. kx201915).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Yang, Q. et al. (2019). Covering Diversification and Fairness for Better Recommendation (Short Paper). In: Wang, X., Gao, H., Iqbal, M., Min, G. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 292. Springer, Cham. https://doi.org/10.1007/978-3-030-30146-0_54
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DOI: https://doi.org/10.1007/978-3-030-30146-0_54
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