# U-rank: Utility-oriented Learning to Rank with Implicit Feedback

arXiv: https://arxiv.org/abs/2011.00550 · HTML: https://mlsanity.com/paper/2011.00550/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2020.

## Built on by (most cited first, 2 of 2)

- [Replace Scoring with Arrangement: A Contextual Set-to-Arrangement Framework for Learning-to-Rank](https://mlsanity.com/paper/2308.02860/) (2023)
- [Beyond Relevance: Utility-Centric Retrieval in the LLM Era](https://mlsanity.com/paper/2604.08920/) (2026)

## Abstract (arXiv)

Learning to rank with implicit feedback is one of the most important tasks in many real-world information systems where the objective is some specific utility, e.g., clicks and revenue. However, we point out that existing methods based on probabilistic ranking principle do not necessarily achieve the highest utility. To this end, we propose a novel ranking framework called U-rank that directly optimizes the expected utility of the ranking list. With a position-aware deep click-through rate prediction model, we address the attention bias considering both query-level and item-level features. Due to the item-specific attention bias modeling, the optimization for expected utility corresponds to a maximum weight matching on the item-position bipartite graph. We base the optimization of this objective in an efficient Lambdaloss framework, which is supported by both theoretical and empirical analysis. We conduct extensive experiments for both web search and recommender systems over three benchmark datasets and two proprietary datasets, where the performance gain of U-rank over state-of-the-arts is demonstrated. Moreover, our proposed U-rank has been deployed on a large-scale commercial recommender and a large improvement over the production baseline has been observed in an online A/B testing.