研究推荐系统在市场中的价值：个性化推荐比公开排名更有效，尤其在需求拥挤时。

Decision-making often requires individuals to navigate large sets of options with incomplete information and imperfectly formed preferences. Information provisioning tools, such as public rankings and personalized recommendations, have become central to guiding these choices, yet their welfare implications across different market environments remain poorly understood. This paper studies a stylized large-market model to quantify the aggregate value of these tools under uncapacitated supply and capacitated supply. Agent utility is a weighted combination of a common term (population-level quality) and an idiosyncratic term (individual-specific fit). Agents observe noisy signals of these components: public rankings improve estimates of common quality, while personalized recommendations additionally reveal idiosyncratic fit. In uncapacitated settings, both tools improve welfare through better selection. Their relative value is governed by preference heterogeneity: rankings are highly effective when preferences are relatively homogeneous, whereas the marginal value of personalization grows as heterogeneity increases. In stark contrast, under capacity constraints, a strict conservation logic applies: learning common quality primarily reshuffles assignments without increasing aggregate average agent welfare. However, revealing idiosyncratic fit generates substantial welfare gains by mitigating congestion on overdemanded items and unlocking match-specific value. We demonstrate that these core insights are robust across model extensions including alternative extreme-value tail distributions and heterogeneous supply-side priorities. Finally, introducing strategic supply-side pricing reveals a stark distributional dichotomy: while information expands total surplus, the newly generated value is ultimately extracted by the short side of the market.