Recommendation generation using personalized weight of meta-paths in heterogeneous information networks
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DOI: 10.1016/j.ejor.2020.01.010
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Cited by:
- Paolo Bartesaghi & Gian Paolo Clemente & Rosanna Grassi, 2021. "A tensor-based unified approach for clustering coefficients in financial multiplex networks," Papers 2105.14325, arXiv.org, revised Apr 2022.
- Paolo Bartesaghi & Gian Paolo Clemente & Rosanna Grassi, 2022. "Clustering coefficients as measures of the complex interactions in a directed weighted multilayer network," Papers 2206.06309, arXiv.org, revised Dec 2022.
- Bartesaghi, Paolo & Clemente, Gian Paolo & Grassi, Rosanna, 2023. "Clustering coefficients as measures of the complex interactions in a directed weighted multilayer network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
- Chen, Zhen-Yu & Fan, Zhi-Ping & Sun, Minghe, 2021. "Tensorial graph learning for link prediction in generalized heterogeneous networks," European Journal of Operational Research, Elsevier, vol. 290(1), pages 219-234.
- K. Coussement & K. W. Bock & S. Geuens, 2022. "A decision-analytic framework for interpretable recommendation systems with multiple input data sources: a case study for a European e-tailer," Annals of Operations Research, Springer, vol. 315(2), pages 671-694, August.
- Park, YoungSoo & Sim, Jeongeun & Kim, Bosung, 2022. "Online retail operations with “Try-Before-You-Buy”," European Journal of Operational Research, Elsevier, vol. 299(3), pages 987-1002.
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Keywords
E-commerce; Networks; Recommendation system; Binary implicit feedback; Decision support;All these keywords.
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