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A Group Clustering Recommendation Approach Based on Energy Distance

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Computational Data and Social Networks (CSoNet 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13831))

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Abstract

Recommendation system focus on the individual recommendation and using relationships between users and users or between items and items by distance or similarity measures. In reality, there are many situations when recommending to individuals is not as important as recommending to groups. Researches on the relationship between a group and a group have not yet been interested in the recommendation. This paper mainly focus on applying the energy distance for group recommendation system. The proposed recommendation model is evaluated on the Jester5k and the MovieLens datasets. The experiment result shows the feasibility of applying the potential energy for the group recommendation problems.

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Notes

  1. 1.

    https://rdrr.io/cran/recommenderlab/man/Jester5k.html.

  2. 2.

    https://rdrr.io/cran/rrecsys/man/ml100k.html.

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Author information

Authors and Affiliations

  1. Vinh Long University of Technology Education, Vinh Long, Vinh Long Province, Vietnam

    Tu Cam Thi Tran

  2. Can Tho University, Can Tho City, Vietnam

    Lan Phuong Phan & Hiep Xuan Huynh

Authors
  1. Tu Cam Thi Tran
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  2. Lan Phuong Phan
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  3. Hiep Xuan Huynh
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Corresponding author

Correspondence to Hiep Xuan Huynh .

Editor information

Editors and Affiliations

  1. Virginia Commonwealth University, Richmond, VA, USA

    Thang N. Dinh

  2. Yeung Kin Man Academic Building, City University Hong Kong, Kowloon Tong, Hong Kong

    Minming Li

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Tran, T.C.T., Phan, L.P., Huynh, H.X. (2023). A Group Clustering Recommendation Approach Based on Energy Distance. In: Dinh, T.N., Li, M. (eds) Computational Data and Social Networks . CSoNet 2022. Lecture Notes in Computer Science, vol 13831. Springer, Cham. https://doi.org/10.1007/978-3-031-26303-3_9

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  • DOI: https://doi.org/10.1007/978-3-031-26303-3_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26302-6

  • Online ISBN: 978-3-031-26303-3

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