{"ID":2885050,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.05093","arxiv_id":"2508.05093","title":"An End-to-End Multi-objective Ensemble Ranking Framework for Video Recommendation","abstract":"We propose a novel End-to-end Multi-objective Ensemble Ranking framework (EMER) for the multi-objective ensemble ranking module, which is the most critical component of the short video recommendation system. EMER enhances personalization by replacing manually-designed heuristic formulas with an end-to-end modeling paradigm. EMER introduces a meticulously designed loss function to address the fundamental challenge of defining effective supervision for ensemble ranking, where no single ground-truth signal can fully capture user satisfaction. Moreover, EMER introduces novel sample organization method and transformer-based network architecture to capture the comparative relationships among candidates, which are critical for effective ranking. Additionally, we have proposed an offline-online consistent evaluation system to enhance the efficiency of offline model optimization, which is an established yet persistent challenge within the multi-objective ranking domain in industry. Abundant empirical tests are conducted on a real industrial dataset, and the results well demonstrate the effectiveness of our proposed framework. In addition, our framework has been deployed in the primary scenarios of Kuaishou, a short video recommendation platform with hundreds of millions of daily active users, achieving a 1.39% increase in overall App Stay Time and a 0.196% increase in 7-day user Lifetime(LT7), which are substantial improvements.","short_abstract":"We propose a novel End-to-end Multi-objective Ensemble Ranking framework (EMER) for the multi-objective ensemble ranking module, which is the most critical component of the short video recommendation system. EMER enhances personalization by replacing manually-designed heuristic formulas with an end-to-end modeling para...","url_abs":"https://arxiv.org/abs/2508.05093","url_pdf":"https://arxiv.org/pdf/2508.05093v2","authors":"[\"Tiantian He\",\"Minzhi Xie\",\"Runtong Li\",\"Xiaoxiao Xu\",\"Jiaqi Yu\",\"Zixiu Wang\",\"Lantao Hu\",\"Han Li\",\"Kun Gai\"]","published":"2025-08-07T07:21:46Z","proceeding":"cs.IR","tasks":"[\"cs.IR\"]","methods":"[\"Transformer\",\"Generative Adversarial Network\"]","has_code":false}