In today’s world, where vast amounts of data are generated daily, providing users with the most relevant information has become increasingly challenging. Recommender systems have therefore attracted significant attention for their ability to predict users’ preferences across a variety of items. While many such systems have been proposed in recent decades, most overlook the benefits of aspect-level review analysis, often leading to suboptimal recommendations. In this thesis, we enhance the performance of review-based recommender systems by integrating both explicit and implicit user–item interactions derived from profiles built on aspect-level sentiments. These profiles are constructed from sentiments expressed in reviews about domain-specific aspects. To achieve this, we leverage DeBERTa (Decoding-enhanced BERT with disentangled attention) for aspect-based sentiment analysis, capturing user preferences from past reviews and item characteristics from public opinion. Our experiments demonstrate that our model outperforms several existing review-based methods by performing fine-grained analysis of reviews, focusing on the most informative segments of the reviews and their associated sentiments, to build robust user and item profiles. This profile construction reduces the system’s reliance on review text, an independence that is particularly valuable in real-world scenarios where predictions must be made for unseen user–item interactions without available reviews.