A Hybrid Approach to Explainable Recommendation: Combining Synthetic Coordinate, Kolmogorov-Arnold Network and Transformer

近年来，可解释推荐因其不仅能提供个性化推荐，还能解释推荐原因而备受关注。基于Transformer的文本生成技术尽管可以生成高度自然的推荐解释，但其高模型复杂性和计算成本也带来了挑战。为应对这些问题，本文提出了一种基于Transformer架构的混合可解释推荐模型。该模型结合了合成坐标和Kolmogorov-Arnold网络(KAN)，通过将用户和物品映射至低维嵌入空间，利用空间关系引导解释生成，实现了高效的评分预测和解释生成。实验结果表明，该模型在评分预测和解释生成任务中均达到了最先进的水平，且在BLEU-4和USR等关键指标上取得了显著提升。研究还揭示了通过优化嵌入向量和模块化设计的结合，能够有效提升可解释推荐系统的性能，同时降低模型复杂性、计算难度及资源消耗。Explainable recommendations have gained attention for offering both personalized suggestions and clear explanations. While Transformer-based text generation produces natural explanations, it introduces challenges of model complexity and computational cost. This paper proposed a hybrid model combining synthetic coordinates and Kolmogorov-Arnold Networks (KAN) with Transformer architecture to address these issues. By mapping users and items into a low-dimensional space and leveraging spatial relationships, the model achieved efficient rating prediction and explanation generation. Experimental results showed state-of-the-art performance, with significant improvements in BLEU-4 and USR metrics. The study demonstrates that optimizing embeddings and using a modular design enhance system performance while reducing complexity and computational difficulty and resource consumption.