Context-aware Recommendation

Context-Aware Recommendation is a recommender-systems approach that extends conventional user–item recommendation by explicitly modeling and incorporating contextual variables (e.g., time, location, companion, device, or task) representing the user’s situational environment into the prediction of item relevance.

2.4K

Publications

145.7K

Citations

6.6K

Authors

1.6K

Institutions

Publications per year

2017–2026

1.2K

Authors

6.6K

  1. FR
    Francesco Ricci⋆

    Free University of Bozen-Bolzano, Italy

    26 publications · 3.5K citations

    19

    h-index

  2. BM
    Bamshad Mobasher

    DePaul University, United States

    21 publications · 3.1K citations

    19

    h-index

  3. RB
    Robin Burke

    DePaul University, United States

    20 publications · 2K citations

    18

    h-index

  4. AT
    Alexander Tuzhilin

    New York University, United States

    20 publications · 12.6K citations

    17

    h-index

  5. WX
    Wayne Xin Zhao

    Renmin University of China, China

    15 publications · 1.7K citations

    15

    h-index

1–5 of 6.6K

Venues

  1. Expert Systems with Applications

    100 publications · 7.6K citations

    44

    h-index

  2. Knowledge-Based Systems

    49 publications · 3.4K citations

    31

    h-index

  3. Information Sciences

    40 publications · 1.8K citations

    27

    h-index

  4. Neurocomputing

    42 publications · 2.1K citations

    27

    h-index

  5. IEEE Access

    51 publications · 2.1K citations

    25

    h-index

1–5