Exact LTI Koopman models for nonlinear control systems require affine linear dynamics under controllability and coordinate inclusion assumptions.
Seq2Slate: Re-ranking and Slate Optimization with RNNs
8 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
abstract
Ranking is a central task in machine learning and information retrieval. In this task, it is especially important to present the user with a slate of items that is appealing as a whole. This in turn requires taking into account interactions between items, since intuitively, placing an item on the slate affects the decision of which other items should be placed alongside it. In this work, we propose a sequence-to-sequence model for ranking called seq2slate. At each step, the model predicts the next `best' item to place on the slate given the items already selected. The sequential nature of the model allows complex dependencies between the items to be captured directly in a flexible and scalable way. We show how to learn the model end-to-end from weak supervision in the form of easily obtained click-through data. We further demonstrate the usefulness of our approach in experiments on standard ranking benchmarks as well as in a real-world recommendation system.
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UniRank unifies autoregressive and non-autoregressive list-wise reranking via bidirectional modeling in a confidence-ordered iterative denoising process, outperforming baselines on datasets and online tests.
NSGR is a tree-structured generative reranker that progressively generates optimal lists via next-scale expansion and multi-scale neighbor loss to balance perspectives and align training signals.
SCASRec unifies ranking and redundancy elimination for route lists via stepwise corrective rewards and an adaptive end-of-recommendation token, claiming SOTA results on two datasets and real deployment.
GloRank reformulates list-wise reranking as token generation over a global item identifier space, using supervised pre-training followed by reinforcement learning to maximize list-wise utility and outperforming baselines on benchmarks and industrial data.
DeGRe decouples offline exploration via a lookahead evaluator using beam search and cumulative regression to distill dense supervision into an online generator that approximates optimal reranking sequences with greedy decoding.
A re-ranking system for rich-media search that plans query intents from sessions, adds visual signals from VLMs, and uses an LLM to score results on multiple facets before multi-task RL adaptation, with reported gains in engagement after industrial deployment.
Dual-Rerank fuses autoregressive and non-autoregressive generative reranking via knowledge distillation and uses list-wise decoupled RL optimization to improve whole-page utility and cut latency in industrial video search.
citing papers explorer
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Limitations of LTI Koopman Modeling for Nonlinear Control Systems
Exact LTI Koopman models for nonlinear control systems require affine linear dynamics under controllability and coordinate inclusion assumptions.
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UniRank: Unified List-wise Reranking via Confidence-Ordered Denoising
UniRank unifies autoregressive and non-autoregressive list-wise reranking via bidirectional modeling in a confidence-ordered iterative denoising process, outperforming baselines on datasets and online tests.
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Next-Scale Generative Reranking: A Tree-based Generative Rerank Method at Meituan
NSGR is a tree-structured generative reranker that progressively generates optimal lists via next-scale expansion and multi-scale neighbor loss to balance perspectives and align training signals.
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SCASRec: A Self-Correcting and Auto-Stopping Model for Generative Route List Recommendation
SCASRec unifies ranking and redundancy elimination for route lists via stepwise corrective rewards and an adaptive end-of-recommendation token, claiming SOTA results on two datasets and real deployment.
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From Local Indices to Global Identifiers: Generative Reranking for Recommender Systems via Global Action Space
GloRank reformulates list-wise reranking as token generation over a global item identifier space, using supervised pre-training followed by reinforcement learning to maximize list-wise utility and outperforming baselines on benchmarks and industrial data.
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DeGRe: Dense-supervised Generative Reranking for Recommendation
DeGRe decouples offline exploration via a lookahead evaluator using beam search and cumulative regression to distill dense supervision into an online generator that approximates optimal reranking sequences with greedy decoding.
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Rich-Media Re-Ranker: A User Satisfaction-Driven LLM Re-ranking Framework for Rich-Media Search
A re-ranking system for rich-media search that plans query intents from sessions, adds visual signals from VLMs, and uses an LLM to score results on multiple facets before multi-task RL adaptation, with reported gains in engagement after industrial deployment.
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Dual-Rerank: Fusing Causality and Utility for Industrial Generative Reranking
Dual-Rerank fuses autoregressive and non-autoregressive generative reranking via knowledge distillation and uses list-wise decoupled RL optimization to improve whole-page utility and cut latency in industrial video search.