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• W2811009292 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." @default.
• W2811009292 created "2018-07-10" @default.
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• W2811009292 date "2018-06-19" @default.
• W2811009292 modified "2023-09-27" @default.
• W2811009292 title "Personalized Group Recommendation Model Based on Argumentation Topic" @default.
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• W2811009292 doi "https://doi.org/10.1007/978-3-319-93659-8_18" @default.
• W2811009292 hasPublicationYear "2018" @default.
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