Recommendation and Temptation

Recommender systems often prioritize engagement over genuine user satisfaction: Traditional systems based on revealed preferences fail to distinguish between what users actually find valuable (enrichment) versus what simply captures their immediate attention (temptation), potentially leading to recommendations that maximize short-term engagement at the expense of long-term wellbeing.

User behavior reflects a fundamental tension between two competing motivations: The paper demonstrates that consumption choices are driven by both inherent value and instant appeal, meaning users may select options they don't truly prefer when tempting alternatives are presented, a dynamic most current systems ignore.

A new recommendation approach can better serve user interests: The researchers developed a model that explicitly accounts for temptation dynamics and proved that a locally greedy recommendation strategy can maximize "consumed enrichment"—the actual value users gain—rather than just clicks or engagement metrics.

The framework performs better than existing alternatives in real-world testing: Evaluation on the MovieLens dataset showed the proposed approach consistently outperformed competitive baselines, suggesting practical viability for implementation in actual recommender systems.

This represents a shift toward more ethical AI design: By aligning recommendations with genuine user satisfaction rather than engagement metrics alone, this work demonstrates how recommender systems can be redesigned to support user autonomy and wellbeing—a key principle for responsible AI development.