Multi-attribute Recommendation

Multi-Attribute Recommendation is a recommendation paradigm that, as a genus, generates personalized suggestions by leveraging auxiliary item or user attributes beyond explicit ratings or binary interactions, and, as a differentia, explicitly integrates multiple heterogeneous attribute types—such as demographic, contextual, or content-based features—into the modeling process to enhance recommendation accuracy and explainability.

49

Publications

2.3K

Citations

174

Authors

74

Institutions

Publications per year

2017–2026

28

Authors

174

  1. LW
    Le Wu

    Hefei University of Technology, China

    3 publications · 196 citations

    3

    h-index

  2. LS
    Lars Schmidt-Thieme

    University of Hildesheim, Germany

    3 publications · 71 citations

    3

    h-index

  3. QL
    Qi Liu

    University of Science and Technology of China, China

    2 publications · 94 citations

    2

    h-index

  4. LC
    Li Chen

    Hong Kong Baptist University, Hong Kong SAR China

    2 publications · 141 citations

    2

    h-index

  5. LH
    Liang He

    East China Normal University, China

    2 publications · 141 citations

    2

    h-index

1–5 of 174

Venues

  1. Expert Systems with Applications

    4 publications · 294 citations

    4

    h-index

  2. Neurocomputing

    4 publications · 98 citations

    4

    h-index

  3. Information Sciences

    4 publications · 176 citations

    4

    h-index

  4. Knowledge-Based Systems

    2 publications · 61 citations

    2

    h-index

  5. IEEE Access

    2 publications · 67 citations

    2

    h-index

1–5