A Comparative Analysis of Text-Based Explainable Recommender Systems

One way to increase trust among users towards recommender systems is to provide the recommendation along with a textual explanation. In the literature, extraction-based, generation-based, and, more recently, hybrid solutions based on retrieval-augmented generation have been proposed to tackle the problem of text-based explainable recommendation. However, the use of different datasets, preprocessing steps, target explanations, baselines, and evaluation metrics complicates the reproducibility and state-of-the-art assessment of previous work among different model categories for successful advancements in the field. Our aim is to provide a comprehensive analysis of text-based explainable recommender systems by setting up a well-defined benchmark that accommodates generation-based, extraction-based, and hybrid approaches. Also, we enrich the existing evaluation of explainability and text quality of the explanations with a novel definition of feature hallucination. Our experiments on three real-world datasets unveil hidden behaviors and confirm several claims about model patterns. Our source code and preprocessed datasets are available at https://github.com/alarca94/text-exp-recsys24.