Abstract
This paper focuses on recommending items to group of users rather than individual users. To model group profile, existing researches almost aggregate preferences of members into a single value, and thus cannot reflect actual group profile of groups with conflicting characteristics. Therefore, we propose a novel group recommender system mechanism. It views group profile as preference distribution, and then models item recommendation process as a multi-criteria decision making process, in order to obtain better recommendation results. Finally, experiments are conducted to verify the proposed approach.
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Acknowledgments
The authors would like to thank the guidance of Professor Wenjia Niu, Professor Chaowei Tang, and Professor Hui Tang. Meanwhile this research is supported by the National Natural Science Foundation of China (No. 61672091).
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Guo, Z., Tang, C., Niu, W., Fu, Y., Xia, H., Tang, H. (2017). Beyond the Aggregation of Its Members—A Novel Group Recommender System from the Perspective of Preference Distribution. In: Li, G., Ge, Y., Zhang, Z., Jin, Z., Blumenstein, M. (eds) Knowledge Science, Engineering and Management. KSEM 2017. Lecture Notes in Computer Science(), vol 10412. Springer, Cham. https://doi.org/10.1007/978-3-319-63558-3_30
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DOI: https://doi.org/10.1007/978-3-319-63558-3_30
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