Prerequisite-aware Recommendation

Prerequisite-Aware Recommendation is a type of recommender system approach that explicitly incorporates prerequisite relationships among learning resources to ensure recommended items are both relevant and sequenced appropriately according to the learner's current knowledge level.

15

Publications

554

Citations

48

Authors

23

Institutions

Publications per year

2017–2026

10

Authors

48

  1. JX
    Jia Xu

    Guangxi University, China

    2 publications · 45 citations

    2

    h-index

  2. PL
    Pin Lv

    Guangxi University, China

    2 publications · 45 citations

    2

    h-index

  3. XW
    Xiaoxin Wang

    Xi'an Jiaotong University, China

    2 publications · 45 citations

    2

    h-index

  4. AP
    Aditya Parameswaran

    Stanford University, United States

    2 publications · 61 citations

    2

    h-index

  5. HG
    Héctor García-Molina

    Stanford University, United States

    2 publications · 61 citations

    2

    h-index

1–5 of 48

Venues

  1. 1

    h-index

  2. IEEE Transactions on Signal Processing

    1 publications · 104 citations

    1

    h-index

  3. Computational Social Networks

    1 publications · 23 citations

    1

    h-index

  4. Smart Learning Environments

    1 publications · 19 citations

    1

    h-index

  5. International Journal of Metadata Semantics and Ontologies

    IJMSO · 1 publications · 10 citations

    1

    h-index

1–5