Exploring Customer Price Preference and Product Profit Role in Recommender Systems
This addresses the problem of aligning recommender systems with business goals for e-commerce companies, though it is incremental as it builds on existing score-based methods.
The paper tackles the gap between academic recommender system metrics and business KPIs like profit by adjusting score-based recommender rankings to incorporate profit and customer price preferences, showing improvements in both precision and profit on fashion e-commerce datasets.
Most of the research in the recommender systems domain is focused on the optimization of the metrics based on historical data such as Mean Average Precision (MAP) or Recall. However, there is a gap between the research and industry since the leading Key Performance Indicators (KPIs) for businesses are revenue and profit. In this paper, we explore the impact of manipulating the profit awareness of a recommender system. An average e-commerce business does not usually use a complicated recommender algorithm. We propose an adjustment of a predicted ranking for score-based recommender systems and explore the effect of the profit and customers' price preferences on two industry datasets from the fashion domain. In the experiments, we show the ability to improve both the precision and the generated recommendations' profit. Such an outcome represents a win-win situation when e-commerce increases the profit and customers get more valuable recommendations.