{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/beyond-kan-introducing-karsein-for-adaptive","title":"CTR-KAN: KAN for Adaptive High-Order Feature Interaction Modeling","arxiv_id":"2408.08713","date":"2024-08-16","proceeding":null,"authors":["Yunxiao Shi","Wujiang Xu","Haimin Zhang","Qiang Wu","Yongfeng Zhang","Min Xu"],"abstract":"Modeling high-order feature interactions is critical for click-through rate (CTR) prediction, yet traditional approaches often face challenges in balancing predictive accuracy and computational efficiency. These methods typically rely on pre-defined interaction orders, which limit flexibility and require extensive prior knowledge. Moreover, explicitly modeling high-order interactions can lead to significant computational overhead. To tackle these challenges, we propose CTR-KAN, an adaptive framework for efficient high-order feature interaction modeling. CTR-KAN builds upon the Kolmogorov-Arnold Network (KAN) paradigm, addressing its limitations in CTR prediction tasks. Specifically, we introduce key enhancements, including a lightweight architecture that reduces the computational complexity of KAN and supports embedding-based feature representations. Additionally, CTR-KAN integrates guided symbolic regression to effectively capture multiplicative relationships, a known challenge in standard KAN implementations. Extensive experiments demonstrate that CTR-KAN achieves state-of-the-art predictive accuracy with significantly lower computational costs. Its sparse network structure also facilitates feature pruning and enhances global interpretability, making CTR-KAN a powerful tool for efficient inference in real-world CTR prediction scenarios.","url_abs":"https://arxiv.org/abs/2408.08713v4","url_pdf":"https://arxiv.org/pdf/2408.08713v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"beyond-kan-introducing-karsein-for-adaptive","repo_url":"https://github.com/ancientshi/karsein","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"click-through-rate-prediction","task_name":"Click-Through Rate Prediction"},{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"kolmogorov-arnold-networks","task_name":"Kolmogorov-Arnold Networks"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"symbolic-regression","task_name":"Symbolic Regression"}],"methods":[{"method_slug":"pruning","method_name":"Pruning"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}