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Title: MCAEM : mixed-correlation analysis-based episodic memory for companion–user interactions
Authors: Zhang, Juzheng
Zheng, Jianmin
Thalmann, Nadia Magnenat
Keywords: Episodic Memory
Engineering::Computer science and engineering
Companion–human Interaction
Issue Date: 2018
Source: Zhang, J., Zheng, J., & Thalmann, N. M. (2018). MCAEM : mixed-correlation analysis-based episodic memory for companion–user interactions. The Visual Computer, 34(6-8), 1129-1141. doi:10.1007/s00371-018-1537-3
Series/Report no.: The Visual Computer
Abstract: This paper considers episodic memory for companion–human interaction, aiming at improving user experience of interactions by endowing social companions with awareness of past experience. Due to noise and incomplete cues from natural language and speech in real-world interaction, accurate memory retrieval is very challenging and the noise resistance is important in practice. To improve the robustness of companion–human interaction, we propose a mixed-correlation analysis-based episodic memory (MCAEM) model, in which the correlations between memory elements are analyzed and then utilized for memory retrieval. In particular, the correlations are analyzed in three aspects: the relations between elements, importance of attributes and order of events. Based on the mixed-correlation analysis, a new similarity measure is constructed, which has substantially enhanced the noise resistance of memory retrieval. Experiments on a dataset collected from interaction in movies quantitatively evaluate the MCAEM model and compare it with prior work. Also, a user study is conducted to investigate the benefits of integrating the MCAEM model into social companions. The results demonstrate that the companions equipped with the MCAEM model not only have better retrieval performance, but also improve user experience in many aspects.
ISSN: 0178-2789
DOI: 10.1007/s00371-018-1537-3
Rights: © 2018 Springer-Verlag GmbH Germany, part of Springer Nature. All rights reserved. This paper was published in The Visual Computer and is made available with permission of Springer-Verlag GmbH Germany, part of Springer Nature.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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