International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | XAI-Based E-Commerce Recommender System |
|---|---|
| ABSTRACT | Personalized recommendation systems help users find relevant products in large e-commerce catalogues but often operate as black boxes with limited transparency. This lack of explainability reduces user trust and adoption. To address this issue, we propose a XAI-Based E-Commerce Recommender System. The system combines collaborative filtering, content-based features, and machine learning–based ranking techniques. Explainability is provided through SHAP-based feature importance and similarity reasoning. The model delivers accurate, user-specific recommendations with clear justifications. Experimental results show improved recommendation quality and increased user trust. |
| AUTHOR | PROF. SREENIVASA B R, CHINMAY S R, SHASHANK J K, TANMAYA T G, MANIKYA D G Professor, Department of Computer Science and Design, Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India B.E Student, Department of Computer Science and Design, Bapuji Institute of Engineering and Technology, Davanagere, Karnataka, India |
| VOLUME | 177 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1312080 |
| pdf/80_XAI-Based E-Commerce Recommender System.pdf | |
| KEYWORDS | |
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