Multi-item Recommendation

Multi-Item Recommendation is a recommendation framework that, as a distinct approach within personalized recommendation systems, generates sets of multiple items simultaneously—rather than individual item suggestions—by modeling interdependencies among items to optimize collective relevance and user satisfaction.

23

Publications

647

Citations

77

Authors

47

Institutions

Publications per year

2017–2026

15

Authors

77

  1. YZ
    Yong Zheng

    Illinois Institute of Technology, United States

    2 publications · 69 citations

    2

    h-index

  2. FG
    Floris Geerts

    University of Edinburgh, United Kingdom

    2 publications · 43 citations

    2

    h-index

  3. TD
    Ting Deng

    Beihang University, China

    2 publications · 43 citations

    2

    h-index

  4. WF
    Wenfei Fan

    University of Edinburgh, United Kingdom

    2 publications · 43 citations

    2

    h-index

  5. MK
    Mihir Kale

    Google (United States), United States

    1 publications · 13 citations

    1

    h-index

1–5 of 77

Venues

  1. Expert Systems with Applications

    2 publications · 95 citations

    2

    h-index

  2. Journal Of Big Data

    1 publications · 15 citations

    1

    h-index

  3. Frontiers of Computer Science

    1 publications · 15 citations

    1

    h-index

  4. ACM Transactions on Information Systems

    1 publications · 15 citations

    1

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 1 publications · 18 citations

    1

    h-index

1–5