Economic-wise, a common goal for companies conducting marketing is to
maximize the return revenue/profit by utilizing the various effective marketing
strategies. Consumer behavior is crucially important in economy and targeted
marketing, in which behavioral economics can provide valuable insights to
identify the biases and profit from customers. Finding credible and reliable
information on products' profitability is, however, quite difficult since most
products tends to peak at certain times w.r.t. seasonal sales cycle in a year.
On-Shelf Availability (OSA) plays a key factor for performance evaluation.
Besides, staying ahead of hot product trends means we can increase marketing
efforts without selling out the inventory. To fulfill this gap, in this paper,
we first propose a general profit-oriented framework to address the problem of
revenue maximization based on economic behavior, and compute the 0n-shelf
Popular and most Profitable Products (OPPPs) for the targeted marketing. To
tackle the revenue maximization problem, we model the k-satisfiable product
concept and propose an algorithmic framework for searching OPPP and its
variants. Extensive experiments are conducted on several real-world datasets to
evaluate the effectiveness and efficiency of the proposed algorithm.

本文提出了一个基于行为经济学的利润导向框架，计算出适用于定向营销的货架上受欢迎和最有利可图的产品，并提出了一种用于搜索 OPPP 及其变体的算法框架。