Recommendation System Effects

Recommendation System Effects is a theoretical construct that denotes the measurable influence of algorithmically generated suggestions on consumers' decision-making processes, behaviors, and preferences within online environments, distinguishing it from general user interface effects by its specificity to personalized or context-aware recommendation mechanisms.

288

Publications

16.6K

Citations

835

Authors

417

Institutions

Publications per year

2017–2026

167

Authors

835

  1. JB
    Jesse Bockstedt

    University of Minnesota, United States

    5 publications · 335 citations

    5

    h-index

  2. GA
    Gediminas Adomavičius

    University of Minnesota, United States

    5 publications · 435 citations

    5

    h-index

  3. KV
    Katrien Verbert

    KU Leuven, Belgium

    5 publications · 316 citations

    5

    h-index

  4. AF
    Alexander Felfernig

    Graz University of Technology, Austria

    4 publications · 112 citations

    4

    h-index

  5. JR
    John Riedl

    University of Minnesota, United States

    4 publications · 640 citations

    4

    h-index

1–5 of 835

Venues

  1. SSRN Electronic Journal

    Repository · 6 publications · 122 citations

    6

    h-index

  2. Information & Management

    6 publications · 415 citations

    6

    h-index

  3. IEEE Access

    7 publications · 273 citations

    6

    h-index

  4. Information Systems Research

    ISR · 5 publications · 481 citations

    5

    h-index

  5. User Modeling and User-Adapted Interaction

    5 publications · 923 citations

    5

    h-index

1–5