Exploring Customer Price Preference and Product Profit Role in Recommender Systems

TL;DR

This paper investigates how incorporating profit awareness and customer price preferences into recommender systems can enhance both recommendation precision and profitability in e-commerce settings.

Contribution

It introduces a method to adjust predicted rankings based on profit and price preferences, bridging the gap between research metrics and industry KPIs.

Findings

Improved recommendation precision with profit-aware adjustments.

Enhanced profit generation without sacrificing recommendation quality.

Effective in fashion e-commerce datasets.

Abstract

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.