# Beyond the Click-Through Rate: Web Link Selection with Multi-level Feedback

arXiv: https://arxiv.org/abs/1805.01702 · HTML: https://mlsanity.com/paper/1805.01702/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2018.

## Built on by (most cited first, 4 of 4)

- [Cost-Effective Online Multi-LLM Selection with Versatile Reward Models](https://mlsanity.com/paper/2405.16587/) (2024)
- [Learning Adversarial MDPs with Stochastic Hard Constraints](https://mlsanity.com/paper/2403.03672/) (2024)
- [A Scalable Recommendation Engine for New Users and Items](https://mlsanity.com/paper/2209.06128/) (2022)
- [Thompson Sampling with Virtual Helping Agents](https://mlsanity.com/paper/2209.08197/) (2022)

## Abstract (arXiv)

The web link selection problem is to select a small subset of web links from a large web link pool, and to place the selected links on a web page that can only accommodate a limited number of links, e.g., advertisements, recommendations, or news feeds. Despite the long concerned click-through rate which reflects the attractiveness of the link itself, the revenue can only be obtained from user actions after clicks, e.g., purchasing after being directed to the product pages by recommendation links. Thus, the web links have an intrinsic multi-level feedback structure. With this observation, we consider the context-free web link selection problem, where the objective is to maximize revenue while ensuring that the attractiveness is no less than a preset threshold. The key challenge of the problem is that each link's multi-level feedbacks are stochastic, and unobservable unless the link is selected. We model this problem with a constrained stochastic multi-armed bandit formulation, and design an efficient link selection algorithm, called Constrained Upper Confidence Bound algorithm (Con-UCB), and prove O(√Tln T) bounds on both the regret and the violation of the attractiveness constraint. We conduct extensive experiments on three real-world datasets, and show that Con-UCB outperforms state-of-the-art context-free bandit algorithms concerning the multi-level feedback structure.