Revenue Maximization and Learning in Product Ranking

The Price of Attention: Ranking Products for Maximum Revenue How should an online retailer rank products when customers have limited attention spans? Chen, Li, and Yang tackle this classic problem by extending the well-known cascade model to account for two crucial, real-world factors: customers view only a random number of items, and the firm’s goal is to maximize revenue, not just clicks. This creates a difficult trade-off between ranking popular, low-price items and riskier, high-price ones. The authors propose the “Best-x” algorithm, an efficient method for finding a near-optimal ranking. They prove it guarantees a revenue of at least 1/e (approximately 37%) of that achievable by a clairvoyant who knows each customer’s attention span in advance. For cases where product attractiveness and attention distributions are unknown, the authors also devise the RankUCB online learning algorithm, which learns personalized rankings from customer interactions and achieves near-optimal performance over time.