Ranking Items by the Current-Preferences and Profits: A List-wise Learning-to-Rank Approach to Profit Maximization

In e-commerce platforms, profit-aware recommender systems aim to improve the platform's profits while maintaining high overall accuracy by recommending items with high profits as top-ranked items. We explore two issues faced by existing model-based profit-aware approaches (i.e., MBAs) when training recommendation models for profit enhancement. First, current MBAs tend to inaccurately infer the item ranking by the profit-based weighting scheme; the ranking of observed (i.e., purchased) items by a user is inferred without considering the user preference for each item, while all unobserved items are assumed to have an equally low ranking. Second, current MBAs train the model without employing the item ranking as ground truth; during training, the model is optimized for the preference score for each item independently rather than being directly optimized for the overall ranking of items. To tackle these issues, we propose a novel MBA that involves three key steps: (S1) defining the Current Preference incorporated with Profit (i.e., CPP) for items; (S2) classifying items through CPP; and (S3) training the model by list-wise learning-to-rank (LTR) based on CPP. Extensive experimental results using real-world platform datasets demonstrate that our approach improves accuracy by approximately 4% and profits by about 24% compared to the best-competing method. CCS Concepts • Information systems → Recommender systems.