Can I read Multi-level Feedback Web Links Selection Problem: Learning and Optimization on EtoBox?

Multi-level Feedback Web Links Selection Problem: Learning and Optimization by Cai, Kechao; Chen, Kun; Huang, Longbo; Lui, John C. S. is a scholarly article available to read on EtoBox.

What is Multi-level Feedback Web Links Selection Problem: Learning and Optimization about?

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 \emph{multi-level feedback structure}. For example, consider a $2$-level feedback web link: the $1$st level feedback provides the Click-Through Rate (CTR) and the $2$nd level feedback provides the potential revenue, which collectively produce the compound $2$-level revenue. We consider the context-free links selection problem of selecting links for a homepage so as to maximize the total compound $2$-level revenue while keeping the total $1$st level feedback above a preset threshold. We further generalize the problem to links with $n~(n\ge2)$-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 \emph{sub-linear} regret and viol

Author

Cai, Kechao; Chen, Kun; Huang, Longbo; Lui, John C. S.

Published

2017

Language

EN