In this paper, we examine the effectiveness of various recommendation strategies in the mobile channel and their impact on consumers' utility and demand levels for individual products. We find significant differences in effectiveness among various recommendation strategies. Interestingly, recommendation strategies that directly embed social proofs for the recommended alternatives outperform other recommendations. Besides, recommendation strategies combining social proofs with higher levels of induced awareness due to the prescribed temporal diversity have an even stronger effect on the mobile channel. In addition, we examine the heterogeneity of the demand effect across items, users, and contextual settings, further verifying empirically the aforementioned information and persuasion mechanisms and generating rich insights. We also facilitate the estimation of causal effects in the presence of endogeneity using machine-learning methods. Specifically, we develop novel econometric instruments that capture product differentiation (isolation) based on deep-learning models of user-generated reviews. Our empirical findings extend the current knowledge regarding the heterogeneous impact of recommender systems, reconcile contradictory prior results in the related literature, and have significant business implications.

中文翻译：

---

移动应用中推荐策略的异类需求效应：来自计量经济学模型和机器学习工具的证据

在本文中，我们研究了移动渠道中各种推荐策略的有效性及其对消费者效用和单个产品需求水平的影响。我们发现各种推荐策略在效果上存在显着差异。有趣的是，直接为所推荐的替代方案嵌入社会证据的推荐策略优于其他建议。此外，由于规定的时间多样性，将社交证据与较高级别的诱导意识相结合的推荐策略对移动通道的影响甚至更大。此外，我们研究了跨项目，用户和上下文设置的需求效应的异质性，进一步凭经验验证了上述信息和说服机制，并产生了丰富的见解。我们还使用机器学习方法，在存在内生性的情况下促进因果效应的估计。具体来说，我们开发了新颖的计量经济学工具，可根据用户生成的评论的深度学习模型来捕获产品差异（隔离）。我们的经验发现扩展了有关推荐系统异质性影响的当前知识，调和了相关文献中相互矛盾的先前结果，并具有重要的业务意义。