Abstract
Recommendation robotics helps users to find similar interests or purposes to those of others. We often provide advice to close friends or similar users, such as sharing favorite dishes, listening to favorite music, etc. In traditional group recommendation robotics, however, users’ personalities have been ignored. In this chapter, a method of group recommendation robotics based on social-trust networks is proposed, which builds a group profile by analyzing not only users’ preferences, but also the social relationships between members inside and outside of the group. We employ a collaborative filter to obtain members’ predictions and adjust the final group preference rating by the external social-trust network if the group has a large disagreement. The experimental results show that the proposed method has a lower root mean square error and leads to a satisfactory effect for the group.
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This work is supported by the National Science Foundation of China under the Grant No.61365010.
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Fang, G., Su, L., Jiang, D., Wu, L. (2020). Group Recommendation Robotics Based on External Social-Trust Networks. In: Lu, H., Yujie, L. (eds) 2nd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-17763-8_7
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DOI: https://doi.org/10.1007/978-3-030-17763-8_7
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