Recommendation Explanation Generation

Recommendation Explanation Generation is the computational process of producing human-readable justifications that articulate the underlying rationale for recommended items within conversational recommendation systems, distinguished by its integration of contextual, user-specific, and item-related factors to enhance transparency and user trust.

111

Publications

8.1K

Citations

341

Authors

132

Institutions

Publications per year

2017–2026

89

Authors

341

  1. YZ
    Yongfeng Zhang

    Rutgers, The State University of New Jersey, United States

    12 publications · 747 citations

    11

    h-index

  2. XX
    Xing Xie

    Microsoft Research Asia (China), China

    6 publications · 529 citations

    6

    h-index

  3. LC
    Li Chen

    Hong Kong Baptist University, Hong Kong SAR China

    6 publications · 485 citations

    6

    h-index

  4. LL
    Lei Li

    Hong Kong Baptist University, Hong Kong SAR China

    5 publications · 455 citations

    5

    h-index

  5. XW
    Xiting Wang

    Microsoft Research Asia (China), China

    4 publications · 469 citations

    4

    h-index

1–5 of 341

Venues

  1. arXiv (Cornell University)

    Repository · 6 publications · 129 citations

    6

    h-index

  2. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 6 publications · 237 citations

    5

    h-index

  3. ACM Transactions on Information Systems

    3 publications · 212 citations

    3

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 108 citations

    3

    h-index

  5. Decision Support Systems

    2 publications · 357 citations

    2

    h-index

1–5