Talos: Optimizing Top-$K$ Accuracy in Recommender Systems

• URL: http://arxiv.org/abs/2601.19276v1
• Date: Tue, 27 Jan 2026 07:04:09 GMT
• Title: Talos: Optimizing Top-$K$ Accuracy in Recommender Systems
• Abstract summary: We propose Talos, a loss function that is specifically designed to optimize the Talos recommendation accuracy.<n>We develop a sampling-based regression algorithm for efficient and accurate threshold estimation.<n>We incorporate a tailored surrogate function to address discontinuity and enhance robustness against distribution shifts.
• Score: 14.229918568280729
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recommender systems (RS) aim to retrieve a small set of items that best match individual user preferences. Naturally, RS place primary emphasis on the quality of the Top-$K$ results rather than performance across the entire item set. However, estimating Top-$K$ accuracy (e.g., Precision@$K$, Recall@$K$) requires determining the ranking positions of items, which imposes substantial computational overhead and poses significant challenges for optimization. In addition, RS often suffer from distribution shifts due to evolving user preferences or data biases, further complicating the task. To address these issues, we propose Talos, a loss function that is specifically designed to optimize the Talos recommendation accuracy. Talos leverages a quantile technique that replaces the complex ranking-dependent operations into simpler comparisons between predicted scores and learned score thresholds. We further develop a sampling-based regression algorithm for efficient and accurate threshold estimation, and introduce a constraint term to maintain optimization stability by preventing score inflation. Additionally, we incorporate a tailored surrogate function to address discontinuity and enhance robustness against distribution shifts. Comprehensive theoretical analyzes and empirical experiments are conducted to demonstrate the effectiveness, efficiency, convergence, and distributional robustness of Talos. The code is available at https://github.com/cynthia-shengjia/WWW-2026-Talos.

Related papers

• Efficiently Estimating Optimal Hyperparameter Scaling Laws through Power-Law Entropy Search [51.33002870475756]
  We introduce Power-Law Entropy Search (PLES), a cost-aware acquisition function built on multi-fidelity Bayesian optimization.<n>PLES searches for candidates that reduce the overall uncertainty of a scaling law estimate, instead of optimizing a single objective function.
  arXiv  Detail & Related papers  (2026-09-01T15:41:59Z)
• Task-CoEvolve: Efficient Harness Optimization via Adaptive Validation Task Selection [51.39973047614183]
  We present a novel approach to efficient LLM harness optimization through adaptive validation task selection.<n>$textbfTask-CoEvolve$, which co-evolves the validation tasks with the harness by addressing two challenges: selecting informative tasks and estimating full-set performance from partial evaluations.
  arXiv  Detail & Related papers  (2026-08-20T15:24:54Z)
• REAR: Test-time Preference Realignment through Reward Decomposition [68.09214603569744]
  We introduce a novel framework that models the task as a realignment problem, since the base model often fails to align with the stated preference.<n>We derive a REAlignment Reward (REAR) that selectively rescales the proportions of these two reward terms.<n>We show that REAR can be formulated as a linear combination of token-level policy log-probabilities, making it computationally efficient and easy to integrate with various TTS algorithms.
  arXiv  Detail & Related papers  (2026-06-29T14:17:53Z)
• OrderGrad: Optimizing Beyond the Mean with Order-Statistic Policy Gradient Estimation [38.52509364819271]
  OrderGrad is a family of likelihood-based gradient estimators for order-statistic objectives.<n>It recovers objectives such as VaR, CVaR, trimmed means, medians, and top-m/best-of-K criteria by changing only the rank weights.<n>We study the resulting estimator's variance behavior and evaluate it on tasks where mean optimization is mismatched to the deployment objective.
  arXiv  Detail & Related papers  (2026-06-04T12:34:15Z)
• Towards Order Fairness: Mitigating LLMs Order Sensitivity through Dual Group Advantage Optimization [20.259122922188126]
  Large Language Models (LLMs) suffer from order bias, where their performance is affected by the arrangement order of input elements.<n>textbfDGAO aims to improve model accuracy and order stability simultaneously.
  arXiv  Detail & Related papers  (2026-05-12T11:31:18Z)
• Adaptive Test-Time Compute Allocation for Reasoning LLMs via Constrained Policy Optimization [18.737087162461563]
  Test-time compute scaling has become a powerful lever for improving large language model performance.<n>Yet deploying these techniques under finite inference budgets requires a decision that current systems largely ignore.<n>We formalize this as a constrained optimization problem (maximize expected accuracy subject to an average compute budget) and solve it with a two-stage Solve-then-Learn pipeline.
  arXiv  Detail & Related papers  (2026-04-16T10:39:22Z)
• DRAFTO: Decoupled Reduced-space and Adaptive Feasibility-repair Trajectory Optimization for Robotic Manipulators [4.0407133618465005]
  This paper introduces a new algorithm for trajectory optimization, Decoupled Reduced-space and Adaptive Feasibility-repair Trajectory Optimization (DRAFTO)<n>To reduce the number of repeated constrained optimizations while handling joint-limit feasibility, the optimization is decoupled into a reduced-space Gauss-Newton (GN) descent.<n>The results of our benchmark tests against optimization-based planners, such as CHOMP, TrajOpt, GPMP2, and FACTO, validate the high efficiency and reliability across diverse scenarios and tasks.
  arXiv  Detail & Related papers  (2026-03-10T20:24:42Z)
• ODAR: Principled Adaptive Routing for LLM Reasoning via Active Inference [60.958331943869126]
  ODAR-Expert is an adaptive routing framework that optimize the accuracy-efficiency trade-off via principled resource allocation.<n>We show strong and consistent gains, including 98.2% accuracy on MATH and 54.8% on Humanity's Last Exam.
  arXiv  Detail & Related papers  (2026-02-27T05:22:01Z)
• SparseEval: Efficient Evaluation of Large Language Models by Sparse Optimization [64.95852289011385]
  Large language models (LLMs) continue to scale up, their performance on various downstream tasks has significantly improved.<n> evaluating their capabilities has become increasingly expensive, as performing inference on a large number of benchmark samples incurs high computational costs.<n>We propose SparseEval, a method that, for the first time, adopts gradient descent to optimize anchor weights and employs an iterative refinement strategy for anchor selection.
  arXiv  Detail & Related papers  (2026-02-08T11:12:45Z)
• ScaLoRA: Optimally Scaled Low-Rank Adaptation for Efficient High-Rank Fine-Tuning [32.55713482636133]
  Low-rank adaptation (LoRA) effectively curtails this cost by confining the weight updates to a low-dimensional subspace.<n>This contribution deals with these limitations by accumulating progressively a high-rank weight update from consecutive low-rank increments.<n>To endow efficient and seamless optimization without restarting, this optimal choice is formed by appropriately scaling the columns of the original low-rank matrix.
  arXiv  Detail & Related papers  (2025-10-27T19:59:46Z)
• On the Optimal Construction of Unbiased Gradient Estimators for Zeroth-Order Optimization [57.179679246370114]
  A potential limitation of existing methods is the bias inherent in most perturbation estimators unless a stepsize is proposed.<n>We propose a novel family of unbiased gradient scaling estimators that eliminate bias while maintaining favorable construction.
  arXiv  Detail & Related papers  (2025-10-22T18:25:43Z)
• Linear Preference Optimization: Decoupled Gradient Control via Absolute Regularization [13.97375970293678]
  DPO (Direct Preference Optimization) has become a widely used offline preference optimization algorithm due to its simplicity and training stability.<n>We propose Linear Preference Optimization (LPO), a novel alignment framework featuring three key innovations.<n>First, we introduce gradient decoupling by replacing the log-sigmoid function with an absolute difference loss, thereby isolating the optimization dynamics.<n>Second, we improve stability through an offset constraint combined with a positive regularization term to preserve the chosen response quality.<n>Third, we implement controllable rejection suppression using gradient separation with straightforward estimation and a tunable coefficient that linearly regulates the descent of the rejection probability.
  arXiv  Detail & Related papers  (2025-08-20T10:17:29Z)
• AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking [35.685682379377134]
  Listwise reranking with large language models (LLMs) enhances top-ranked results in retrieval-based applications.<n>We propose AcuRank, an adaptive reranking framework that dynamically adjusts both the amount and target of computation based on uncertainty estimates over document relevance.<n>Results on the TREC-DL and BEIR benchmarks show that our method consistently achieves a superior accuracy-efficiency trade-off and scales better with compute than fixed-computation baselines.
  arXiv  Detail & Related papers  (2025-05-24T05:15:49Z)
• "Over-optimizing" for Normality: Budget-constrained Uncertainty Quantification for Contextual Decision-making [1.2997358113120427]
  We study uncertainty quantification for contextual optimization using weighted sample average approximation (wSAA)<n>We establish central limit theorems for wSAA and construct algorithm-normality-based confidence intervals for optimal conditional expected costs.<n>We show that over-optimizing'' (running more iterations than the nominal rule) mitigates this misspecification and preserves normality, at the expense of a slight slowdown in the convergence rate of the budget-constrained estimator.
  arXiv  Detail & Related papers  (2025-03-17T02:31:56Z)
• Semiparametric conformal prediction [79.6147286161434]
  We construct a conformal prediction set accounting for the joint correlation structure of the vector-valued non-conformity scores.<n>We flexibly estimate the joint cumulative distribution function (CDF) of the scores.<n>Our method yields desired coverage and competitive efficiency on a range of real-world regression problems.
  arXiv  Detail & Related papers  (2024-11-04T14:29:02Z)
• Geometric-Averaged Preference Optimization for Soft Preference Labels [78.2746007085333]
  Many algorithms for aligning LLMs with human preferences assume that human preferences are binary and deterministic.<n>In this work, we introduce the distributional soft preference labels and improve Direct Preference Optimization (DPO) with a weighted geometric average of the LLM output likelihood in the loss function.
  arXiv  Detail & Related papers  (2024-09-10T17:54:28Z)
• Adaptive Preference Scaling for Reinforcement Learning with Human Feedback [103.36048042664768]
  Reinforcement learning from human feedback (RLHF) is a prevalent approach to align AI systems with human values. We propose a novel adaptive preference loss, underpinned by distributionally robust optimization (DRO) Our method is versatile and can be readily adapted to various preference optimization frameworks.
  arXiv  Detail & Related papers  (2024-06-04T20:33:22Z)
• Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits [12.674929126684528]
  We address the problem of semi-bandits, where a player selects among P actions from the power set of a set containing d base items. We show that our approach efficiently leverages the semi-bandit feedback and outperforms bandit feedback approaches.
  arXiv  Detail & Related papers  (2024-02-23T08:07:54Z)
• A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport [92.96250725599958]
  Kernel-based optimal transport (OT) estimators offer an alternative, functional estimation procedure to address OT problems from samples. We show that our SSN method achieves a global convergence rate of $O (1/sqrtk)$, and a local quadratic convergence rate under standard regularity conditions.
  arXiv  Detail & Related papers  (2023-10-21T18:48:45Z)
• Adaptive Neural Ranking Framework: Toward Maximized Business Goal for Cascade Ranking Systems [33.46891569350896]
  Cascade ranking is widely used for large-scale top-k selection problems in online advertising and recommendation systems. Previous works on learning-to-rank usually focus on letting the model learn the complete order or top-k order. We name this method as Adaptive Neural Ranking Framework (abbreviated as ARF)
  arXiv  Detail & Related papers  (2023-10-16T14:43:02Z)
• Recommendation Systems with Distribution-Free Reliability Guarantees [83.80644194980042]
  We show how to return a set of items rigorously guaranteed to contain mostly good items. Our procedure endows any ranking model with rigorous finite-sample control of the false discovery rate. We evaluate our methods on the Yahoo! Learning to Rank and MSMarco datasets.
  arXiv  Detail & Related papers  (2022-07-04T17:49:25Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.