Abstract
We presented in this paper a usage scenario in which cultural resources in a public context, items on display in a historical museum for instance, should be recommended to groups of visitors in response to their interest (or preferences), thus conserving computational resources and reducing network traffic. Motivated by the scenario, we set out to design and implement a group recommender system, Museum Guides for Groups (MGG), that provides visitors to a museum with a sequence of items of interest by efficiently clustering visitors of similar user profiles into groups and computing recommendations for each group. Our work in progress was reported, focusing on the system design and the selection of an appropriate clustering algorithm for dividing visitors. We evaluated the efficiency of three candidate clustering techniques, including the bisecting K-Means, DBSCAN, and improved CURE, using the MovieLens dataset with 1M ratings.
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Acknowledgments
This work was funded by the Open Project Program of Jiangsu Engineering Center of Network Monitoring and Nanjing University of Information Science and Technology Project (PAPD and CICAEET). The authors would also like to acknowledge the input of the National Key Technology R & D Program (No. 2015BAK25B03).
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Zhou, J., Xie, W., Zhang, C. (2016). Towards Interest-Based Group Recommendation for Cultural Resource Sharing. In: Sun, X., Liu, A., Chao, HC., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2016. Lecture Notes in Computer Science(), vol 10040. Springer, Cham. https://doi.org/10.1007/978-3-319-48674-1_31
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DOI: https://doi.org/10.1007/978-3-319-48674-1_31
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