Talos: Optimizing Top-$K$ Accuracy in Recommender Systems
This work addresses a key bottleneck in recommender systems for improving user experience by efficiently optimizing Top-K metrics, though it is incremental as it builds on existing optimization methods.
The paper tackles the challenge of optimizing Top-K accuracy in recommender systems, which is computationally expensive and sensitive to distribution shifts, by proposing Talos, a loss function that uses quantile techniques and sampling-based regression to achieve up to 15% improvement in Precision@K and Recall@K on benchmark datasets.
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.