Selecting the right web links for a website is important because appropriate links not only can provide high attractiveness but can also increase the website's revenue. In this work, we first show that web links have an intrinsic . For example, consider a @math -level feedback web link: the @math st level feedback provides the Click-Through Rate (CTR) and the @math nd level feedback provides the potential revenue, which collectively produce the compound @math -level revenue. We consider the context-free links selection problem of selecting links for a homepage so as to maximize the total compound @math -level revenue while keeping the total @math st level feedback above a preset threshold. We further generalize the problem to links with @math -level feedback structure. The key challenge is that the links' multi-level feedback structures are unobservable unless the links are selected on the homepage. To our best knowledge, we are the first to model the links selection problem as a constrained multi-armed bandit problem and design an effective links selection algorithm by learning the links' multi-level structure with provable regret and violation bounds. We uncover the multi-level feedback structures of web links in two real-world datasets. We also conduct extensive experiments on the datasets to compare our proposed algorithm with two state-of-the-art context-free bandit algorithms and show that algorithm is the most effective in links selection while satisfying the constraint.
One common approach to the links selection problem is to perform A B testing @cite_3 , which splits the traffic for different sets of links on two different web pages, and then evaluate their rewards. However, A B testing does not have any loss regret guarantee as it splits equal amounts of traffic to the links regardless of the links' rewards. That said, A B testing is still widely used in commercial web systems. Our algorithm can be viewed as a complementary approach to A B testing, e.g., our algorithm can select the set of links with the @math st level reward above a given threshold and facilitate a more efficient A B testing for the links selection problem.
@cite_3
2799539834
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 . 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 (), and prove @math bounds on both the regret and the violation of the attractiveness constraint. We conduct extensive experiments on three real-world datasets, and show that outperforms state-of-the-art context-free bandit algorithms concerning the multi-level feedback structure.