{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T20:18:39Z","timestamp":1778271519813,"version":"3.51.4"},"publisher-location":"Singapore","reference-count":35,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819203628","type":"print"},{"value":"9789819203635","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-981-92-0363-5_5","type":"book-chapter","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T19:53:49Z","timestamp":1778270029000},"page":"71-87","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient Personalized Reranking with\u00a0Semi-autoregressive Generation and\u00a0Online Knowledge Distillation"],"prefix":"10.1007","author":[{"given":"Kai","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwen","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yawen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,9]]},"reference":[{"key":"5_CR1","doi-asserted-by":"crossref","unstructured":"Ai, Q., Bi, K., Guo, J., Croft, W.B.: Learning a deep listwise context model for ranking refinement. In: The 41st international ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 135\u2013144 (2018)","DOI":"10.1145\/3209978.3209985"},{"key":"5_CR2","doi-asserted-by":"crossref","unstructured":"Ai, Q., Wang, X., Bruch, S., Golbandi, N., Bendersky, M., Najork, M.: Learning groupwise multivariate scoring functions using deep neural networks. In: Proceedings of the 2019 ACM SIGIR International Conference on Theory of Information Retrieval, pp. 85\u201392 (2019)","DOI":"10.1145\/3341981.3344218"},{"key":"5_CR3","unstructured":"Bello, I., et al.: Seq2slate: re-ranking and slate optimization with RNNs. arXiv preprint arXiv:1810.02019 (2018)"},{"key":"5_CR4","doi-asserted-by":"crossref","unstructured":"Carraro, D., Bridge, D.: Enhancing recommendation diversity by re-ranking with large language models. ACM Trans. Recommender Syst. (2024)","DOI":"10.1145\/3700604"},{"key":"5_CR5","unstructured":"Deng, J., et al.: OneRec: unifying retrieve and rank with generative recommender and iterative preference alignment. arXiv preprint arXiv:2502.18965 (2025)"},{"key":"5_CR6","unstructured":"Feng, Y., Gong, Y., Sun, F., Ge, J., Ou, W.: Revisit recommender system in the permutation prospective. arXiv preprint arXiv:2102.12057 (2021)"},{"key":"5_CR7","unstructured":"Feng, Y., Hu, B., Gong, Y., Sun, F., Liu, Q., Ou, W.: GRN: generative rerank network for context-wise recommendation. arXiv preprint arXiv:2104.00860 (2021)"},{"key":"5_CR8","doi-asserted-by":"crossref","unstructured":"Gong, X., Feng, Q., Zhang, Y., Qin, J., Ding, W., Li, B., Jiang, P., Gai, K.: Real-time short video recommendation on mobile devices. In: Proceedings of the 31st ACM international conference on information & knowledge management. pp. 3103\u20133112 (2022)","DOI":"10.1145\/3511808.3557065"},{"key":"5_CR9","doi-asserted-by":"publisher","unstructured":"Han, F., Wang, S., Zhao, J., Wu, R., Rui, X., Wang, Z.: Fair re-ranking recommendation based on debiased multi-graph representations. In: International Conference on Advanced Data Mining and Applications, pp. 168\u2013182. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-46661-8_12","DOI":"10.1007\/978-3-031-46661-8_12"},{"key":"5_CR10","first-page":"2744","volume":"34","author":"J Hron","year":"2021","unstructured":"Hron, J., Krauth, K., Jordan, M., Kilbertus, N.: On component interactions in two-stage recommender systems. Adv. Neural. Inf. Process. Syst. 34, 2744\u20132757 (2021)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"issue":"2","key":"5_CR11","first-page":"1214","volume":"35","author":"G Huzhang","year":"2021","unstructured":"Huzhang, G., et al.: Aliexpress learning-to-rank: maximizing online model performance without going online. IEEE Trans. Knowl. Data Eng. 35(2), 1214\u20131226 (2021)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"4","key":"5_CR12","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1145\/582415.582418","volume":"20","author":"K J\u00e4rvelin","year":"2002","unstructured":"J\u00e4rvelin, K., Kek\u00e4l\u00e4inen, J.: Cumulated gain-based evaluation of IR techniques. ACM Trans. Inf. Syst. (TOIS) 20(4), 422\u2013446 (2002)","journal-title":"ACM Trans. Inf. Syst. (TOIS)"},{"key":"5_CR13","doi-asserted-by":"crossref","unstructured":"Li, Y., et al.: Pear: personalized re-ranking with contextualized transformer for recommendation. In: Companion Proceedings of the Web Conference 2022, pp. 62\u201366 (2022)","DOI":"10.1145\/3487553.3524208"},{"key":"5_CR14","doi-asserted-by":"crossref","unstructured":"Lin, X., et al.: Discrete conditional diffusion for reranking in recommendation. In: Companion Proceedings of the ACM on Web Conference 2024, pp. 161\u2013169 (2024)","DOI":"10.1145\/3589335.3648313"},{"issue":"8","key":"5_CR15","first-page":"7782","volume":"35","author":"Z Lin","year":"2022","unstructured":"Lin, Z., et al.: Attention over self-attention: intention-aware re-ranking with dynamic transformer encoders for recommendation. IEEE Trans. Knowl. Data Eng. 35(8), 7782\u20137795 (2022)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"5_CR16","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: Generative flow network for listwise recommendation. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1524\u20131534 (2023)","DOI":"10.1145\/3580305.3599364"},{"key":"5_CR17","doi-asserted-by":"crossref","unstructured":"Liu, W., Liu, Q., Tang, R., Chen, J., He, X., Heng, P.A.: Personalized re-ranking with item relationships for e-commerce. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 925\u2013934 (2020)","DOI":"10.1145\/3340531.3412332"},{"key":"5_CR18","doi-asserted-by":"crossref","unstructured":"Liu, W., et al.: Neural re-ranking in multi-stage recommender systems: a review. arXiv preprint arXiv:2202.06602 (2022)","DOI":"10.24963\/ijcai.2022\/771"},{"key":"5_CR19","doi-asserted-by":"crossref","unstructured":"Pang, L., Xu, J., Ai, Q., Lan, Y., Cheng, X., Wen, J.: Setrank: learning a permutation-invariant ranking model for information retrieval. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 499\u2013508 (2020)","DOI":"10.1145\/3397271.3401104"},{"key":"5_CR20","doi-asserted-by":"crossref","unstructured":"Pei, C., et\u00a0al.: Personalized re-ranking for recommendation. In: Proceedings of the 13th ACM Conference on Recommender Systems, pp. 3\u201311 (2019)","DOI":"10.1145\/3298689.3347000"},{"key":"5_CR21","doi-asserted-by":"crossref","unstructured":"Ren, Y., Yang, Q., Wu, Y., Xu, W., Wang, Y., Zhang, Z.: Non-autoregressive generative models for reranking recommendation. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 5625\u20135634 (2024)","DOI":"10.1145\/3637528.3671645"},{"key":"5_CR22","unstructured":"Shen, T., et al.: P-Law: predicting quantitative scaling law with entropy guidance in large recommendation models. In: The Thirty-ninth Annual Conference on Neural Information Processing Systems"},{"key":"5_CR23","doi-asserted-by":"crossref","unstructured":"Shi, X., et al.: Pier: permutation-level interest-based end-to-end re-ranking framework in e-commerce. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4823\u20134831 (2023)","DOI":"10.1145\/3580305.3599886"},{"key":"5_CR24","unstructured":"Stern, M., Shazeer, N., Uszkoreit, J.: Blockwise parallel decoding for deep autoregressive models. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"5_CR25","doi-asserted-by":"crossref","unstructured":"Wang, C., Zhang, J., Chen, H.: Semi-autoregressive neural machine translation. arXiv preprint arXiv:1808.08583 (2018)","DOI":"10.18653\/v1\/D18-1044"},{"key":"5_CR26","doi-asserted-by":"crossref","unstructured":"Wang, S., et al.: NLGR: utilizing neighbor lists for generative rerank in personalized recommendation systems. In: Companion Proceedings of the ACM on Web Conference 2025, pp. 530\u2013537 (2025)","DOI":"10.1145\/3701716.3715251"},{"key":"5_CR27","unstructured":"Weng, M., et al.: Beyond positive history: re-ranking with list-level hybrid feedback. arXiv preprint arXiv:2410.20778 (2024)"},{"key":"5_CR28","unstructured":"Xi, Y., et al.: Context-aware reranking with utility maximization for recommendation. arXiv preprint arXiv:2110.09059 (2021)"},{"key":"5_CR29","doi-asserted-by":"crossref","unstructured":"Xi, Y., et al.: Multi-level interaction reranking with user behavior history. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1336\u20131346 (2022)","DOI":"10.1145\/3477495.3532026"},{"key":"5_CR30","doi-asserted-by":"crossref","unstructured":"Xia, H., Ge, T., Wang, P., Chen, S.Q., Wei, F., Sui, Z.: Speculative decoding: exploiting speculative execution for accelerating seq2seq generation. arXiv preprint arXiv:2203.16487 (2022)","DOI":"10.18653\/v1\/2023.findings-emnlp.257"},{"key":"5_CR31","doi-asserted-by":"crossref","unstructured":"Yin, M., et al.: Dataset regeneration for sequential recommendation. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 3954\u20133965 (2024)","DOI":"10.1145\/3637528.3671841"},{"key":"5_CR32","unstructured":"Yu, H., et al.: Thought-augmented planning for LLM-powered interactive recommender agent. arXiv preprint arXiv:2506.23485 (2025)"},{"key":"5_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, J., et al.: Td3: tucker decomposition based dataset distillation method for sequential recommendation. In: Proceedings of the ACM on Web Conference 2025, pp. 3994\u20134003 (2025)","DOI":"10.1145\/3696410.3714613"},{"key":"5_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, L., et al.: Killing two birds with one stone: unifying retrieval and ranking with a single generative recommendation model. In: Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2224\u20132234 (2025)","DOI":"10.1145\/3726302.3730017"},{"key":"5_CR35","doi-asserted-by":"crossref","unstructured":"Zhuang, T., Ou, W., Wang, Z.: Globally optimized mutual influence aware ranking in e-commerce search. arXiv preprint arXiv:1805.08524 (2018)","DOI":"10.24963\/ijcai.2018\/518"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-0363-5_5","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T19:53:58Z","timestamp":1778270038000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-0363-5_5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819203628","9789819203635"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-0363-5_5","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"9 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jeju","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Korea (Republic of)","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 April 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 April 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/dasfaa2026.github.io\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}