Cross-channel Recommendation

Cross-Channel Recommendation is a recommendation framework that integrates user behavior and contextual data across multiple interactive platforms or touchpoints to generate personalized suggestions, distinguished by its capacity to unify disparate channel-specific signals into coherent, cross-environment recommendation strategies.

6

Publications

246

Citations

21

Authors

9

Institutions

Publications per year

2017–2026

4

Authors

21

  1. BD
    Beibei Dong

    Lehigh University, United States

    1 publications · 57 citations

    1

    h-index

  2. LC
    Laura Christiansen

    DePaul University, United States

    1 publications · 14 citations

    1

    h-index

  3. JG
    Jonathan Gemmell

    DePaul University, United States

    1 publications · 14 citations

    1

    h-index

  4. JQ
    Ju Qiu

    Central South University, China

    1 publications · 18 citations

    1

    h-index

  5. MH
    Minxue Huang

    Wuhan University, China

    2 publications · 114 citations

    1

    h-index

1–5 of 21

Venues

  1. Journal of Retailing and Consumer Services

    2 publications · 107 citations

    2

    h-index

  2. Journal of Marketing

    JM · 1 publications · 57 citations

    1

    h-index

  3. Electronic Commerce Research and Applications

    1 publications · 34 citations

    1

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 1 publications · 34 citations

    1

    h-index

  5. Information Systems Research

    ISR · 1 publications · 19 citations

1–5