Recommendation Systems

Recommendation Systems is an information-filtering and decision-support methodology in customer analytics, data mining, e-commerce systems, and machine learning that models users and items from observed data to predict and rank unseen items by their expected relevance or utility for individual users, thereby generating personalized content or product suggestions at scale.

13.3K

Publications

908.4K

Citations

25.9K

Authors

3.9K

Institutions

Publications per year

2017–2026

6.8K

Authors

25.9K

  1. XH
    Xiangnan He

    University of Science and Technology of China, China

    104 publications · 27.4K citations

    57

    h-index

  2. BM
    Bamshad Mobasher

    DePaul University, United States

    76 publications · 7.8K citations

    47

    h-index

  3. JA
    Joseph A. Konstan

    University of Minnesota, United States

    63 publications · 38.9K citations

    46

    h-index

  4. RB
    Robin Burke

    DePaul University, United States

    75 publications · 6K citations

    43

    h-index

  5. HY
    Hongzhi Yin

    The University of Queensland, Australia

    70 publications · 6.5K citations

    43

    h-index

1–5 of 25.9K

Venues

  1. Expert Systems with Applications

    381 publications · 26.6K citations

    90

    h-index

  2. Knowledge-Based Systems

    202 publications · 15K citations

    69

    h-index

  3. arXiv (Cornell University)

    Repository · 379 publications · 28.4K citations

    68

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 164 publications · 25K citations

    62

    h-index

  5. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 144 publications · 12.7K citations

    57

    h-index

1–5