###### Abstract  
In recent years, recommender systems--which provide personalized recommendations by analyzing users' historical behavior to infer their preferences--have become essential tools across various domains, including e-commerce, streaming media, and social platforms. Recommender systems play a crucial role in enhancing user experience by mining vast amounts of data to identify what is most relevant to users. Among these, deep learning-based recommender systems have demonstrated exceptional recommendation performance. However, these \"black-box\" systems lack reasonable explanations for their recommendation results, which reduces their impact and credibility. To address this situation, an effective strategy is to provide a personalized textual explanation along with the recommendation. This approach has received increasing attention from researchers because it can enhance users' trust in recommender systems through intuitive explanations. In this context, our paper introduces a novel explainable recommendation model named GCLTE. This model integrates Graph Contrastive Learning with transformers within an Encoder-Decoder framework to perform rating prediction and reason generation simultaneously. In addition, we cleverly combine the neural network layer with the transformer using a straightforward information enhancement operation. Finally, our extensive experiments on three real-world datasets demonstrate the effectiveness of GCLTE in both recommendation and explanation. The experimental results show that our model outperforms the top existing models. Keywords:explainable recommendation; multi-task learning; contrastive learning; rating prediction; reason generation; recommendation algorithms +
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(CL) significantly enhances performance [2; 3]. Graph data augmentation strategies create diverse user-item interaction graphs, providing additional supervised signals [4; 5]. However, performance gains in CL-based recommendation models primarily stem from the contrastive loss mechanism, specifically InfoNCE [6], a form of Noise Contrastive Estimation that is widely used in Contrastive Learning to maximize the mutual information between positive sample pairs, rather than graph-based strategies [7]. However, while deep learning has expanded possibilities in recommender systems, it has also increased the opacity of these models, thus complicating their explainability [8; 9]. We believe that if the recommender system provides a personalised explanation consistent with the user's rating prediction, this will significantly enhance user trust and subsequently increase the system's effectiveness. Recommendation explanation methods are categorized into two types: embedded and post hoc [9; 10]. Embedded methods modify the recommender system's structure to enhance explainability [11; 12; 13; 14], while post hoc methods use an external model to explain opaque recommendation models [15; 16; 17; 18]. Embedded methods aim to enhance prediction performance and internal transparency, revealing the model's decision making process but often failing to deliver high-quality recommendation explanations [10]. Post hoc methods generate explanations for complex models by creating separate modules, decoupling the recommendation and explanation tasks. This enhances model design flexibility and the explainability of high-performance models that are difficult to explain. In recent years, several explainable recommendation methods based on post-processing techniques have been developed. These methods generate natural text explanations for personalized recommendations using natural language processing [19; 20; 21; 22]. We note that the Encoder-Decoder-based model offers good portability and structural simplicity. Effective results are achieved by transferring embedded latent representations between two tasks within this straightforward multi-task learning framework [15; 16].