Abstract
Group argumentation support system provides a human-machine intelligent argumentation environment to solve complex problems. Through personalized recommendation, the thinking of participants can be inspired and guided, the efficiency of group argumentation can be improved, and the group intelligence can be embodied better. This paper explores how to recommend personalized items for groups and puts forward a group item recommendation model based on the topic of argumentation. Firstly, the current argumentation subject is extracted and the users holding the similar views are clustered into a group. Next, the BP neural network is used for content based recommendation to cope with the cold start problem. At the same time, the coarse recommended data is used to increase the recommended efficiency. During the process, a group preference model is also built around the topic keywords. Then, the collaborative filtering algorithm based on topic is used to get the final intelligent recommendation results. Finally, the validity of the model is proved by the comparison experiments with the MovieLens data set.
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Acknowledgements
This research is supported by National Key Research and Development Program of China under grant number 2017YFC1405400, and National Natural Science Foundation of China under grant number 61075059, 61300127, and Green Industry Technology Leading Project (product development category) of Hubei University of Technology under grant number CPYF2017008.
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Xiong, C., Lv, K., Wang, H., Qi, C. (2019). Personalized Group Recommendation Model Based on Argumentation Topic. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_18
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DOI: https://doi.org/10.1007/978-3-319-93659-8_18
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