# A Profit-Maximizing Strategy for Advertising on the e-Commerce Platforms

arXiv: https://arxiv.org/abs/2211.01160 · HTML: https://mlsanity.com/paper/2211.01160/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 31 Oct 2022.

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

- [Product-based Neural Networks for User Response Prediction](https://mlsanity.com/paper/1611.00144/) (2016)
- [Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](https://mlsanity.com/paper/1601.02376/) (2016)
- [Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising](https://mlsanity.com/paper/1305.3011/) (2013)
- [Profit Maximization for Online Advertising Demand-Side Platforms](https://mlsanity.com/paper/1706.01614/) (2017)
- [Optimal Bidding, Allocation and Budget Spending for a Demand Side Platform Under Many Auction Types](https://mlsanity.com/paper/1805.11645/) (2018)

## Abstract (arXiv)

The online advertising management platform has become increasingly popular among e-commerce vendors/advertisers, offering a streamlined approach to reach target customers. Despite its advantages, configuring advertising strategies correctly remains a challenge for online vendors, particularly those with limited resources. Ineffective strategies often result in a surge of unproductive ``just looking'' clicks, leading to disproportionately high advertising expenses comparing to the growth of sales. In this paper, we present a novel profit-maximing strategy for targeting options of online advertising. The proposed model aims to find the optimal set of features to maximize the probability of converting targeted audiences into actual buyers. We address the optimization challenge by reformulating it as a multiple-choice knapsack problem (MCKP). We conduct an empirical study featuring real-world data from Tmall to show that our proposed method can effectively optimize the advertising strategy with budgetary constraints.