Content-based Cold-start Recommendation

Content-Based Cold-Start Recommendation is a recommendation approach that addresses the cold-start problem by leveraging item or user attribute information to generate personalized suggestions without relying on historical interaction data.

31

Publications

1.1K

Citations

111

Authors

55

Institutions

Publications per year

2017–2026

22

Authors

111

  1. AB
    Alper Bilge

    Anadolu University, Türkiye

    2 publications · 21 citations

    2

    h-index

  2. CK
    Cihan Kaleli

    Eskişehir City Hospital, Türkiye

    2 publications · 21 citations

    2

    h-index

  3. AY
    Ali Yürekli

    Eskisehir Technical University, Türkiye

    2 publications · 21 citations

    2

    h-index

  4. MP
    Michael Pulis

    University of Malta, Malta

    1 publications · 34 citations

    1

    h-index

  5. FG
    Felipe G. Contratres

    Universidade de São Paulo, Brazil

    1 publications · 18 citations

    1

    h-index

1–5 of 111

Venues

  1. Mathematical Problems in Engineering

    MPE · 1 publications · 43 citations

    1

    h-index

  2. World Wide Web

    1 publications · 15 citations

    1

    h-index

  3. Neurocomputing

    1 publications · 18 citations

    1

    h-index

  4. ACM Transactions on Interactive Intelligent Systems

    TIIS · 1 publications · 26 citations

    1

    h-index

  5. Big Data and Cognitive Computing

    1 publications · 25 citations

    1

    h-index

1–5