Memory-Forgetting Model based on Virtual and Real Spaces for Commercial Recommendation

Yunlan Xue; Lingyu Xu; Jie Yu; Lei Wang; Gaowei Zhang

Workshop on the Crowdsensing and intelligent sensing on Mobile Media Analytics

Research Article

Memory-Forgetting Model based on Virtual and Real Spaces for Commercial Recommendation

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/eai.18-6-2016.2264332,
    author={Yunlan Xue and Lingyu Xu and Jie Yu and Lei Wang and Gaowei Zhang},
    title={Memory-Forgetting Model based on Virtual and Real Spaces for Commercial Recommendation},
    proceedings={Workshop on the Crowdsensing and intelligent sensing on Mobile Media Analytics},
    publisher={ACM},
    proceedings_a={SMMA},
    year={2016},
    month={12},
    keywords={social media; crowdsensing; event; episode; memory-forgetting curve; information influence},
    doi={10.4108/eai.18-6-2016.2264332}
}

Yunlan Xue
Lingyu Xu
Jie Yu
Lei Wang
Gaowei Zhang
Year: 2016
Memory-Forgetting Model based on Virtual and Real Spaces for Commercial Recommendation
SMMA
ACM
DOI: 10.4108/eai.18-6-2016.2264332

Yunlan Xue^,*, Lingyu Xu¹, Jie Yu¹, Lei Wang¹, Gaowei Zhang¹

1: School of Computer Engineering and Science, Shanghai University

*Contact email: xueyunlan@i.shu.edu.cn

Abstract

With the social media development, semantic web and Crowdsensing blog platforms become the store of big data, which store the memory of diverse information. Memory enables past experiences to be remembered and acquired as useful knowledge to support decision making in commercial recommendations, especially when perception and computational resources are limited in Crowdsensing. However, the traditional event research lacks the view of memory and forgetting of event from the people’s cognitive psychology. On the basis of Ebbinghaus, we treat every day’s information as an independent event to calculate Memory-Forgetting Curve (MFC), then calculate MFC of episode with the fusion of events’.

Keywords: social media; crowdsensing; event; episode; memory-forgetting curve; information influence

Published: 2016-12-12
Publisher: ACM

: http://dx.doi.org/10.4108/eai.18-6-2016.2264332