Rationale Generation

Rationale Generation is the computational process of producing human-interpretable justifications that explain the underlying reasons for a recommendation, distinguished from other recommendation components by its focus on transparency and post-hoc interpretability in user-facing explanations.

104

Publications

4.2K

Citations

321

Authors

132

Institutions

Publications per year

2017–2026

54

Authors

321

  1. RA
    Rana Alkadhi

    Technical University of Munich, Germany

    4 publications · 157 citations

    4

    h-index

  2. BB
    Bernd Bruegge

    Technical University of Munich, Germany

    4 publications · 157 citations

    4

    h-index

  3. TW
    Tianfu Wu

    North Carolina State University, United States

    3 publications · 56 citations

    3

    h-index

  4. EG
    Emitzá Guzmán

    Technical University of Munich, Germany

    3 publications · 95 citations

    3

    h-index

  5. XS
    Xi Song

    Shenzhen University, China

    3 publications · 56 citations

    3

    h-index

1–5 of 321

Venues

  1. arXiv (Cornell University)

    Repository · 11 publications · 437 citations

    11

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 3 publications · 75 citations

    3

    h-index

  3. 2

    h-index

  4. 2

    h-index

  5. 1

    h-index

1–5