可解释推荐的混合方法：结合合成坐标、Kolmogorov-Arnold Network和Transformer
A Hybrid Approach to Explainable Recommendation: Combining Synthetic Coordinate, Kolmogorov-Arnold Network and Transformer
DOI: 10.12677/mos.2025.145370, PDF,    国家自然科学基金支持
作者: 艾 均, 徐戈辉, 苏 湛, 苏 杭：上海理工大学光电信息与计算机工程学院，上海
关键词: 可解释推荐；合成坐标；Kolmogorov-Arnold Network；Transformer；Explainable Recommendation； Synthetic Coordinates； Kolmogorov–Arnold Network； Transformer
摘要: 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。
Abstract: 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.
文章引用：艾均, 徐戈辉, 苏湛, 苏杭. 可解释推荐的混合方法：结合合成坐标、Kolmogorov-Arnold Network和Transformer[J]. 建模与仿真, 2025, 14(5): 13-30. https://doi.org/10.12677/mos.2025.145370

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