User-Based Collaborative Filtering Multi-criteria Recommender System Based on Interaction Between Criteria, Criteria Set with Choquet Integral

Author:

Huynh Tri Minh,Tran Vu The,Huynh Hiep Xuan

Publisher

Springer Nature Switzerland

Reference23 articles.

1. Michael, D.E., John, T.R., Joseph, A.K.: Collaborative filtering recommender systems. Hum. Comput. Interact. 4, pp. 291–324. (2011). https://doi.org/10.1561/1100000009

2. Tri, H.M., Vu, T.T., Hung, H.H., Hiep, X.H.: Decision making operations: arithmetic mean, geometric mean, harmonic mean in user-based collaborative filtering of multi-criteria recommender systems. In: The 6th national scientific conference on information technology and applications in various fields, Da Nang, VietNam (2017).

3. Badrul, S., George, K., Joseph, K., John, R.: Item-based Collaborative Filtering Recommendation Algorithms. Appears in WWW10, May 1–5, Hong Kong (2001)

4. Michael, H.: Recommenderlab: A Framework for Developing and Testing Recommendation Algorithms, R package version 0.2–2 (2007). http://lyle.smu.edu/IDA/recommenderlab/

5. Tri, H.M., Vu, T.T., Hung, H.H., Hiep, X.H.: Collaborative filtering recommender system base on the interaction multi-criteria decision with ordered weighted averaging operator. In: Proceedings of 2018 The 2nd International Conference on Machine Learning and Soft Computing ICMLSC 2018, ACM ISBN: 978–1–4503–6336–5, pp. 57–61 (2018).

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