A hierarchical recommendation system for E-commerce using online user reviews

Irem Islek*, Sule Gunduz Oguducu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)

Abstract

Recommendation systems are considered as one of the important components of e-commerce platforms due to their direct impact on profitability. In this study, we propose a hierarchical recommendation system to increase the performance of the e-commerce recommendation system. Our DeepIDRS approach has a two-level hierarchical structure: (1) The first level uses bidirectional encoder representations to represent textual information of an item (title, description, and a subset of item reviews), efficiently and accurately; (2) The second level is an attention-based sequential recommendation model that uses item embeddings derived from the first level of the hierarchical structure. Furthermore, we compare our approach DeepIDRS with various approaches from different perspectives. Our results in the real-world dataset show that DeepIDRS provides at least 10% better HR@10 and NCCG@10 performance than other review-based models. With this study, for e-commerce, we clearly show that a hierarchical, explainable recommendation system that accurately represents the item title, description, and a subset of item reviews, improves performance.

Original languageEnglish
Article number101131
JournalElectronic Commerce Research and Applications
Volume52
DOIs
Publication statusPublished - 1 Mar 2022

Bibliographical note

Publisher Copyright:
© 2022 Elsevier B.V.

Keywords

  • Bidirectional encoder representations
  • Deep Learning
  • Hierarchical recommendation models
  • Sequential recommendation models

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