Recommendation and Temptation

TL;DR

This paper introduces a new recommender system model that balances long-term user enrichment with short-term temptation, improving recommendation quality by explicitly modeling user behavior and preferences.

Contribution

It proposes a behavioral model and a novel recommendation objective that explicitly account for the duality of user preferences, incorporating off-platform options and providing an optimal greedy strategy.

Findings

Outperforms baselines ignoring temptation dynamics

Demonstrates effectiveness on synthetic and real-world data

Aligns recommendations with long-term user enrichment

Abstract

Traditional recommender systems based on revealed preferences often fail to capture the fundamental duality in user behavior, where consumption choices are driven by both inherent value (enrichment) and instant appeal (temptation). Consequently, these systems may generate recommendations that prioritize short-term engagement over long-lasting user satisfaction. We propose a novel recommender design that explicitly models the tension between enrichment and temptation. We introduce a behavioral model that accounts for how both enrichment and temptation influence user choices, while incorporating the reality of off-platform alternatives. Building on this model, we formulate a novel recommendation objective aligned with maximizing consumed enrichment and prove the optimality of a locally greedy recommendation strategy. Finally, we present an estimation framework that leverages the distinction between explicit user feedback and implicit choice data while making minimal assumptions about off-platform options. Through comprehensive evaluation using both synthetic simulations and real-world data from the MovieLens dataset, we demonstrate that our approach consistently outperforms competitive baselines that ignore temptation dynamics either by assuming revealed preferences or recommending solely based on enrichment. Our work represents a paradigm shift toward more nuanced and user-centric recommender design, with significant implications for developing responsible AI systems that genuinely serve users' long-term interests rather than merely maximizing engagement.