Shi, Y., Larson, M., & Hanjalic, A. (2014). Collaborative Filtering beyond the User Item Matrix: Research on the State of the Art and Future Challenges. ACM Computing Surveys, 47, Article No.: 3.
https://doi.org/10.1145/2556270

has been cited by the following article:

• TITLE: Empirical Evidence during the Implementation of an Educational Chatbot with the Electroencephalogram Metric

  AUTHORS: Andreia Solange Bos, Michelle Câmara Pizzato, Marcelo Vettori, Lucília Gomes Donato, Paula Poli Soares, James Gladstone Fagundes, Milton Antônio Zaro

  KEYWORDS: Chatbot, Attention, Eeg, Neurosky

  JOURNAL NAME: Creative Education, Vol.11 No.11, November 24, 2020

  ABSTRACT: A student’s attention status during a digital conversation can differ when talking to an intelligent agent, that is, a chatterbot interactive. There is evidence that brain waves have different oscillations during the study. A virtual platform with an artificial intelligence chatterbot was explored in this study. During the study, participants were asked to use the Neurosky Mindwave sensor to assess their attention levels in real time. Statistical analyses of the results revealed significant effects on the participants’ attention levels. The chatbot was associated with dominant proportions of delta and theta brain waves in didactic content. Conversation via social interaction chatbot triggered dominant gamma brain waves.