Price and Profit Awareness in Recommender Systems
This addresses the need for online retailers to optimize profit rather than just user relevance, though it is incremental as it builds on existing recommendation methods.
The paper tackles the problem that traditional recommender systems maximize user utility but not provider profit, and demonstrates through simulations that incorporating price and profit information can yield significant business benefits.
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.