Beyond the Click-Through Rate: Web Link Selection with Multi-level Feedback
Refereed conference paper presented and published in conference proceedings


摘要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 emph{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 (myucb), and prove $O(sqrt{Tln T})$ bounds on both the regret and the violation of the attractiveness constraint. We conduct extensive experiments on three real-world datasets, and show that myucb{} outperforms state-of-the-art context-free bandit algorithms concerning the multi-level feedback structure.
著者Kun Chen, Kechao Cai, Longbo Huang, John C.S. Lui
會議名稱International Joint Conferences on Artificial Intelligence (IJCAI), 2018
會議論文集題名Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI 2018

上次更新時間 2018-29-06 於 09:17