Contextual Preference Learning

Contextual Preference Learning is a machine learning framework that models user preferences as a function of contextual variables, distinguishing itself from general preference learning by explicitly incorporating situational, temporal, or environmental factors to adapt recommendations dynamically.

41

Publications

2.2K

Citations

121

Authors

54

Institutions

Publications per year

2017–2026

21

Authors

121

  1. EQ
    Elisa Quintarelli

    Politecnico di Milano, Italy

    2 publications · 82 citations

    2

    h-index

  2. HC
    Huanhuan Cao

    University of Science and Technology of China, China

    2 publications · 213 citations

    2

    h-index

  3. MV
    Mihaela van der Schaar

    University of California, Los Angeles, United States

    2 publications · 127 citations

    2

    h-index

  4. LT
    Letizia Tanca

    Politecnico di Milano, Italy

    2 publications · 82 citations

    2

    h-index

  5. HZ
    Hengshu Zhu

    Baidu (China), China

    2 publications · 213 citations

    2

    h-index

1–5 of 121

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 251 citations

    2

    h-index

  2. Pharmacology Biochemistry and Behavior

    1 publications · 14 citations

    1

    h-index

  3. Applied Sciences

    1 publications · 10 citations

    1

    h-index

  4. Universal Access in the Information Society

    1 publications · 16 citations

    1

    h-index

  5. IEEE Transactions on Services Computing

    1 publications · 106 citations

    1

    h-index

1–5