Collaborative filtering is a widely used method in recommender systems research. However, contrary to the assumption that it relies solely on rating data, many contemporary models incorporate review information to address issues such as data sparsity. Although previous recommender systems utilised review texts to capture user preferences and item features, they often rely on a single‐embedding model to represent these features, which may limit the richness of the extracted information. Recent advancements suggest that combining multiple pre‐trained embedding models can enhance text representation by leveraging the strengths of different encoding methods. In this study, we propose a novel recommender system model, the Multi‐embedding Fusion Network for Recommendation (MFNR), which employs a multi‐embedding approach to effectively capture and represent user and item features in review texts. Specifically, the proposed model integrates Bidirectional Encoder Representations from Transformers (BERT) and its optimised variant, RoBERTa, both of which are pre‐trained transformer‐based models designed for natural language understanding. By leveraging their contextual embeddings, our model extracts enriched feature representations from review texts. Extensive experiments conducted on real‐world review datasets from Amazon.com and Goodreads.com demonstrate that MFNR significantly outperforms existing baseline models, achieving an average improvement of 9.18% in RMSE and 14.81% in MAE. These results highlight the efficacy of the multi‐embedding approach, indicating its potential for broader application in complex recommendation scenarios.

With the growth of the internet and e-commerce, online reviews have become a prevalent and rich source of information for personalized recommendations. Review text typically contains user preferences regarding various aspects of items or services. However, most previous recommendation models that used online reviews focused on general opinions, without considering the various aspects of the review text. Although previous approaches have effectively reflected the overall preferences embedded in reviews, capturing the preferences for various aspects is limited. This paper proposes an attentive aspect-based recommendation model with a deep neural network (AARN), which can capture user preferences regarding various aspects embedded in review text. The proposed model uses an advanced aspect-based sentiment analysis (ABSA) method. Unlike previous approaches that extract overall preferences, ABSA offers the advantage of extracting preferences for various aspects embedded in review texts. An attention mechanism is then applied to measure the unique attention weights for each aspect. Extensive experiments were conducted on real-world online review datasets, showing that the proposed model outperformed baseline models in effectively predicting user ratings for target items. Furthermore, this study demonstrated the influence of the ABSA method on personalized and explainable recommendations.

With the rapid advancement of information and communication technology, the number of items users encounter increased exponentially. Consequently, the importance of recommendation systems emerged to reduce the time and effort required for users to make item selections. Recently, among various studies on recommendation systems, there has been significant interest in leveraging review text as auxiliary information. This study proposes a novel model to enhance recommendation performance by effectively analyzing review texts through the fusion of local and global features. By combining convolutional neural networks (CNN), which excel in extracting local features, and the RoBERTa model, renowned for capturing global contextual features, the proposed approach effectively uncovers users’ latent preferences embedded within review texts. The proposed model comprises three key components: the user–item interaction module, which learns complex interactions between users and items; the feature extraction module, which extracts both local and global features using CNN and RoBERTa; and the preference prediction module, which combines the output vectors from the previous modules to predict user preferences for specific items. Extensive experiments conducted on three datasets collected from Amazon platform demonstrate that the proposed model significantly outperforms baseline models. These findings highlight the effectiveness of the proposed approach in considering both local and global features for extracting user preferences from review texts.

Review text in recommender systems provides rich insights into user preferences and experiences that cannot be fully captured by numerical ratings alone. While recent studies have increasingly leveraged review text to enhance recommendation accuracy, most have primarily focused on improving model performance, with limited attention to quantitatively examining how specific textual elements influence rating prediction. To address this gap, this study empirically investigates the impact of review text characteristics on prediction performance in review-based recommender systems. Specifically, we employ the Unstructured Context-Aware Model (UCAM), where contextual information is replaced with review text embedded using a pre-trained BERT model. Three key textual factors are examined: review length, aspect, and emotion type. Review length is divided into quartiles, and results show that removing shorter reviews significantly degrades performance, indicating their critical role. For analysis, reviews are categorized into food, service, price, atmosphere, and location, with service and food contributing most to performance improvements, while location shows relatively low influence. Emotion types are classified based on Plutchik’s framework, revealing that removing joy, trust, and anticipation reduces performance, whereas excluding sadness slightly improves it. Overall, this study highlights the differential importance of textual features and demonstrates their potential for enhancing recommender system design.

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.