Graph Attention-Based Engineering Framework for Aspect-Level Sentiment-Driven Recommendation Using BiLSTM–CRF Hybrid Model

Abstract

The rapid expansion of user-generated content in the form of reviews and ratings on e-commerce sites calls for sophisticated recommendation algorithms that can comprehend both the sentiments expressed in text and numeric evaluations. Existing recommendation strategies based on collaborative filtering and content-based filtering have been shown to neglect the meaning conveyed in user reviews, resulting in poor performance and interpretability. In this study, we introduce a novel recommendation algorithm that combines Aspect-Based Sentiment Analysis (ABSA) and a Graph Attention Network (GAT) to improve the recommendation quality. Initially, user reviews are processed by a Bidirectional Long Short-Term Memory (BiLSTM) model supplemented with an attention mechanism to obtain detailed sentiments toward aspects and generate an implied rating score. The difference between the implicit and explicit rating scores is then used to estimate sentiment-ratings consistency. Finally, a GAT is introduced to explore intricate relationships among users, items, and sentiment dimensions by weighting useful relations and ignoring noises through attention coefficients. The proposed approach is based on the aggregation of sentiments, scores, and embeddings. Experimental results on multiple benchmark data sets show that the proposed method for hybrid recommendations outperforms other approaches in terms of precision, and recall. Moreover, the proposed framework also allows for improved interpretability because of the aspect-level sentiment representation.