# Towards Revenue Maximization with Popular and Profitable Products

arXiv: https://arxiv.org/abs/2202.13041 · HTML: https://mlsanity.com/paper/2202.13041/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 54 references that are in the map.
- First posted on arXiv 26 Feb 2022.

## Builds on (most cited first, 5 of 5)

- [Explainable Recommendation: A Survey and New Perspectives](https://mlsanity.com/paper/1804.11192/) (2018)
- [A Survey of Utility-Oriented Pattern Mining](https://mlsanity.com/paper/1805.10511/) (2018)
- [Privacy Preserving Utility Mining: A Survey](https://mlsanity.com/paper/1811.07389/) (2018)
- [Who Benefits from the "Sharing" Economy of Airbnb?](https://mlsanity.com/paper/1602.02238/) (2016)
- [An Effective Marketing Strategy for Revenue Maximization with a Quantity Constraint](https://mlsanity.com/paper/1505.06286/) (2015)

## Abstract (arXiv)

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.