Retail Recommendation Algorithms

Retail Recommendation Algorithms is a computational method within the field of digital merchandising that systematically generates personalized product suggestions to consumers by analyzing behavioral, contextual, and transactional data to optimize retail outcomes such as sales, engagement, and customer satisfaction.

72

Publications

4.5K

Citations

222

Authors

107

Institutions

Publications per year

2017–2026

35

Authors

222

  1. JL
    Jing Li

    Chinese University of Hong Kong, Hong Kong SAR China

    3 publications · 335 citations

    3

    h-index

  2. WZ
    Weinan Zhang

    Shanghai Jiao Tong University, China

    2 publications · 80 citations

    2

    h-index

  3. JY
    Junjie Yao

    Peking University, China

    2 publications · 285 citations

    2

    h-index

  4. TM
    Takahiro Miyashita

    Advanced Telecommunications Research Institute International, Japan

    2 publications · 125 citations

    2

    h-index

  5. KS
    Kazuhiko Shinozawa

    Advanced Telecommunications Research Institute International, Japan

    2 publications · 125 citations

    2

    h-index

1–5 of 222

Venues

  1. Electronic Commerce Research and Applications

    3 publications · 280 citations

    3

    h-index

  2. Journal of Retailing

    3 publications · 713 citations

    3

    h-index

  3. Procedia Computer Science

    3 publications · 97 citations

    3

    h-index

  4. Expert Systems with Applications

    3 publications · 370 citations

    3

    h-index

  5. Journal of Retailing and Consumer Services

    2 publications · 200 citations

    2

    h-index

1–5