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DisCo: Towards Harmonious Disentanglement and Collaboration between Tabular and Semantic Space for Recommendation.

, , , , , , , , and . CoRR, (2024)

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Multi-Level Interaction Reranking with User Behavior History., , , , , , , , and . SIGIR, page 1336-1346. ACM, (2022)On-device Integrated Re-ranking with Heterogeneous Behavior Modeling., , , , , , , and . KDD, page 5225-5236. ACM, (2023)How Can Recommender Systems Benefit from Large Language Models: A Survey., , , , , , , , , and 1 other author(s). CoRR, (2023)Personalized Diversification for Neural Re-ranking in Recommendation., , , , , , , and . ICDE, page 802-815. IEEE, (2023)ClickPrompt: CTR Models are Strong Prompt Generators for Adapting Language Models to CTR Prediction., , , , , , , , , and . WWW, page 3319-3330. ACM, (2024)A Bird's-eye View of Reranking: from List Level to Page Level., , , , , , , and . CoRR, (2022)Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models., , , , , , , and . CoRR, (2024)Beyond Relevance Ranking: A General Graph Matching Framework for Utility-Oriented Learning to Rank., , , , , , , , and . ACM Trans. Inf. Syst., 40 (2): 25:1-25:29 (2022)Utility-Oriented Reranking with Counterfactual Context., , , , , , and . ACM Trans. Knowl. Discov. Data, 18 (8): 193:1-193:22 (September 2024)Towards Open-World Recommendation with Knowledge Augmentation from Large Language Models., , , , , , , , and . CoRR, (2023)