A SURVEY OF TAXONOMY-AWARE  RECOMMENDER SYSTEMS IN E-COMMERCE

Dang Nhat Minh; Dang Van Tien; Nguyen Tien Duong; Nguyen Thu Ha

Dang Nhat Minh Hung Yen University of Technology and Education
Dang Van Tien Hung Yen University of Technology and Education
Nguyen Tien Duong Hung Yen University of Technology and Education
Nguyen Thu Ha Hung Yen University of Technology and Education

Abstract

Recommender systems are essential in modern e-commerce for alleviating information overload and delivering personalized product suggestions. However, many existing methods treat products as independent entities and ignore the hierarchical taxonomy structure that naturally organizes items, limiting their ability to generalize user preferences, especially in sparse and cold-start scenarios. This paper provides a systematic survey of taxonomy-aware recommender systems in e-commerce, categorizing existing approaches into four groups: taxonomy-based feature engineering, taxonomy-aware representation learning, taxonomy aware reasoning, and taxonomy construction and refinement. For each category, we analyze representative studies in terms of methodology, strengths, and limitations, and offer practical guidance for method selection. Finally, we highlight key challenges, including taxonomy quality, scalability, and multi-source data integration, and outline promising directions for future research.

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