Steering via algorithmic recommendations
研究亚马逊作为平台所有者和零售商的双重角色下，其自营产品在“经常一起购买”推荐中更受青睐，并通过库存缺货实验证明这种引导行为降低了推荐效率。
Abstract This article studies self‐preferencing in algorithmic recommendations on dominant platforms, focusing on Amazon's dual role as platform owner and retailer. We find that products sold by Amazon receive substantially more “Frequently Bought Together” recommendations across popularity deciles. To establish causality, we exploit within‐product variation generated by Amazon stockouts. We find that when Amazon is out of stock, identical products sold by third‐party sellers face an eight‐percentage‐point decrease in the probability of receiving a recommendation. The pattern can be explained by the economic incentives of steering but not explained by consumer preference. Furthermore, the steering lowers recommendation efficiency.