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As a result, conventional CTR models face a persistent tension between enriching representations with complex high-order interactions and keeping computation tractable. To address this dual challenge, this study introduces the Kolmogorov\u2013Arnold Represented Sparse Efficient Interaction Network (KarSein). Drawing inspiration from the learnable activation mechanism in the Kolmogorov\u2013Arnold Network (KAN), KarSein leverages this mechanism to adaptively transform low-order basic features into high-order feature interactions, offering a novel approach to feature interaction modeling. KarSein extends the capabilities of KAN by introducing a more efficient architecture that significantly reduces computational costs while accommodating 2D embedding vectors as feature inputs. Furthermore, it overcomes the limitation of KAN\u2019s its inability to spontaneously capture multiplicative relationships among features. Extensive experiments highlight the superiority of KarSein, demonstrating its ability to surpass not only the vanilla implementation of KAN in CTR prediction tasks but also other baseline methods. Remarkably, KarSein achieves exceptional predictive accuracy while maintaining a highly compact parameter size and minimal computational overhead. Moreover, KarSein retains the key advantages of KAN, such as strong interpretability and structural sparsity. As the first systematic adaptation of KAN to CTR prediction, KarSein offers a practical, parameter-efficient, and interpretable alternative for modeling complex feature interactions in large-scale recommendation systems.<\/jats:p>","DOI":"10.1145\/3799425","type":"journal-article","created":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T08:53:49Z","timestamp":1772268829000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Beyond KAN: Introducing KarSein for Adaptive High-Order Feature Interaction Modeling in CTR Prediction"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1516-015X","authenticated-orcid":false,"given":"Yunxiao","family":"Shi","sequence":"first","affiliation":[{"name":"School of Electrical and Data Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3500-1068","authenticated-orcid":false,"given":"Wujiang","family":"Xu","sequence":"additional","affiliation":[{"name":"Rutgers University, New Brunswick, New Jersey, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0021-3634","authenticated-orcid":false,"given":"Haimin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Electrical and Data Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5641-2483","authenticated-orcid":false,"given":"Qiang","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electrical and Data Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9581-8849","authenticated-orcid":false,"given":"Min","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Electrical and Data Engineering, Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2026,5,11]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3481915"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3448218.3448232"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Heng-Tze Cheng Levent Koc Jeremiah Harmsen Tal Shaked Tushar Chandra Hrishi Aradhye Glen Anderson Greg Corrado Wei Chai Mustafa Ispir et al. 2016. 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