Long-tail Recommendation

Long-Tail Recommendation is a recommendation approach that targets items from the less popular, niche segment of the item distribution, thereby distinguishing itself from mainstream or popularity-biased recommendation methods by focusing on low-frequency but collectively significant items.

74

Publications

5.1K

Citations

238

Authors

109

Institutions

Publications per year

2017–2026

53

Authors

238

  1. HA
    Himan Abdollahpouri

    DePaul University, United States

    6 publications · 765 citations

    6

    h-index

  2. BM
    Bamshad Mobasher

    DePaul University, United States

    6 publications · 765 citations

    6

    h-index

  3. RB
    Robin Burke

    DePaul University, United States

    6 publications · 765 citations

    6

    h-index

  4. LH
    Lichan Hong

    Google (United States), United States

    4 publications · 377 citations

    4

    h-index

  5. EH
    Ed H.

    Google (United States), United States

    4 publications · 377 citations

    4

    h-index

1–5 of 238

Venues

  1. arXiv (Cornell University)

    Repository · 9 publications · 401 citations

    9

    h-index

  2. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 4 publications · 442 citations

    4

    h-index

  3. Knowledge-Based Systems

    4 publications · 261 citations

    3

    h-index

  4. ACM Transactions on Information Systems

    3 publications · 119 citations

    3

    h-index

  5. 2

    h-index

1–5