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Applying uncertainty theory to group recommender systems taking account of experts preferences

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Abstract

This study aims to generate recommendations for a group by weighting the preferences of each group member. We applied uncertain statistics to the preference scores of a panel of experts and a genetic algorithm (GA) to balance the weights of the group members (UGA). A group profile was then designed on the basis of the user ratings and the scores of the experts. A novel similarity measure was then developed based on uncertainty theory to refine the number of K-nearest neighbors (KNN). By integrating uncertain statistics and the novel similarity measure, group profiles were developed from the MovieLens and Gym datasets. Experiments demonstrated that the proposed approach was significantly better than two baseline approaches in generating group recommendations.

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Notes

  1. http://orsc.edu.cn/online/

  2. http://movielens.umn.edu

  3. https://www.dianping.com/tianjin/sports

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Acknowledgements

We gratefully acknowledge that this work is financed by the National Natural Science Foundation of China (grant numbers 71271147, 71671121).

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Authors and Affiliations

  1. College of Management of Economics, Tianjin University, Tianjin, 300072, China

    Junpeng Guo, Lihua Sun, Wenhua Li & Ting Yu

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  1. Junpeng Guo
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  2. Lihua Sun
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  3. Wenhua Li
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  4. Ting Yu
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Correspondence to Lihua Sun.

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Guo, J., Sun, L., Li, W. et al. Applying uncertainty theory to group recommender systems taking account of experts preferences. Multimed Tools Appl 77, 12901–12918 (2018). https://doi.org/10.1007/s11042-017-4922-4

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  • DOI: https://doi.org/10.1007/s11042-017-4922-4

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