# Price and Profit Awareness in Recommender Systems

arXiv: https://arxiv.org/abs/1707.08029 · HTML: https://mlsanity.com/paper/1707.08029/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2017.

## Built on by (most cited first, 7 of 7)

- [Fairness in Recommender Systems: Research Landscape and Future Directions](https://mlsanity.com/paper/2205.11127/) (2022)
- [Balancing Consumer and Business Value of Recommender Systems: A Simulation-based Analysis](https://mlsanity.com/paper/2203.05952/) (2022)
- [Interpolating Item and User Fairness in Multi-Sided Recommendations](https://mlsanity.com/paper/2306.10050/) (2023)
- [Multi-Objective Recommender Systems: Survey and Challenges](https://mlsanity.com/paper/2210.10309/) (2022)
- [Recommender Systems: A Primer](https://mlsanity.com/paper/2302.02579/) (2023)
- [GNN-GMVO: Graph Neural Networks for Optimizing Gross Merchandise Value in Similar Item Recommendation](https://mlsanity.com/paper/2310.17732/) (2023)
- [Value-Aware Product Recommendation by Customer Segmentation using a suitable High-Dimensional Similarity Measure](https://mlsanity.com/paper/2604.26983/) (2026)

## Abstract (arXiv)

Academic research in the field of recommender systems mainly focuses on the problem of maximizing the users' utility by trying to identify the most relevant items for each user. However, such items are not necessarily the ones that maximize the utility of the service provider (e.g., an online retailer) in terms of the business value, such as profit. One approach to increasing the providers' utility is to incorporate purchase-oriented information, e.g., the price, sales probabilities, and the resulting profit, into the recommendation algorithms. In this paper we specifically focus on price- and profit-aware recommender systems. We provide a brief overview of the relevant literature and use numerical simulations to illustrate the potential business benefit of such approaches.