XTRA: eXplainable Text-based Recommendation Algorithm

Recommendation systems built on ratings often fail to fully exploit the rich information contained in textual reviews and social-platform content. Text provides not only an additional signal for ranking, but also a natural basis for generating explanations. This study introduces XTRA, a rating-free, text-driven recommendation system that derives user relationships from shared review content and transforms sentiment expressed in text into personalized recommendation decisions. The proposed approach also incorporates explainability mechanisms to clarify both why users are considered similar and how textual sentiment contributes to the final recommendation. Experimental results on Amazon-Beauty, Yelp-Illinois, and IMDB-20K show that XTRA outperforms strong rating-based baselines on Yelp and IMDB, while remaining competitive on Amazon. These findings indicate that textual signals can serve as an effective alternative to ratings while also supporting transparent and interpretable recommendations. Implementation details are provided to support reproducibility and future extension.