Sentiment-aware Rating-based Recommendation via Semantic-enhanced Item Alignment

Abstract

Leveraging review texts to mine deep user preferences is vital for recommendation. However, existing methods neglect the positive-negative counteraction and rely on noisy hard sentiment thresholds. Furthermore, the feature density asymmetry causes dense semantic features to overwhelm sparse collaborative signals. To address these issues, we propose the SENSE model for fine-grained sentiment-aware and structurally aligned recommendation. We employ a confidence-aware soft sentiment assignment and a sentiment exchange mechanism to quantify fine-grained preferences and explicitly model sentiment interaction. For feature fusion, we construct a collaborative-semantic graph and incorporate adaptive data augmentation. Finally, via anchor-guided cross-view alignment, we use the reliable behavior view as a topological anchor to force semantic alignment, thereby preserving semantic richness while preventing collaborative signals from being overwhelmed.

Through experiments on four datasets, we verify that SENSE outperforms state-of-the-art methods. It achieves average improvements of 19.26% in MSE for rating prediction, 13.41% in Recall@10 and 13.89% in NDCG@10 for Top-N recommendation. Our source code is available at https://github.com/yingjie160/SENSE.