Enhancing E‐Commerce Recommendations Through Review Summarization and Multi‐Embedding Feature Fusion

ABSTRACT

With the rapid growth of e‐commerce, recommender systems have become essential tools for alleviating information overload by providing users with personalized item suggestions that match their preferences. Traditional collaborative filtering approaches have shown strong performance, while they still suffer from data sparsity since they rely solely on limited rating information. To address this problem, recent studies have incorporated user reviews as supplementary textual data, which contain rich semantic and emotional information reflecting user preferences and item characteristics. However, review texts often include noisy and irrelevant content that can degrade recommendation performance. To overcome this limitation, this study proposes a Summarization‐based Key Information‐Aware Recommendation (SKAR) model that integrates extractive summarization and multi‐embedding techniques to improve rating prediction accuracy. The proposed model first employs a TextRank‐based summarization module to extract key information from user and item reviews and reduce noise. It then applies a multi‐embedding module that fuses textual representations from BERT and RoBERTa to capture richer semantic features and reduce the representation bias arising from a single pretrained language model. Finally, a multi‐layer perceptron module predicts user ratings by modelling non‐linear user–item interactions. Experiments on three real‐world Amazon datasets show that the proposed SKAR model outperforms various baseline models. These results confirm the effectiveness of incorporating review summarization and multi‐embedding strategies in review‐based recommendation.