摘要翻译：
推荐系统是在产品和购买者之间产生匹配的必要成分。尽管它们无处不在，但它们面临两个重要挑战。首先，它们是数据密集型的，这一特点排除了某些类型的卖家（包括那些出售耐用品的卖家）的复杂建议。其次，他们经常专注于估计消费者对产品的固定评价，而忽略了营销文献中确定的依赖状态的行为。我们提出了一个基于消费者浏览行为的推荐系统，它绕过了上述的“冷启动”问题，并考虑到消费者作为“移动目标”的事实，根据在搜索过程中向他们推荐的建议而不同地表现出不同的行为。首先，通过机器学习方法恢复用户的搜索策略函数。其次，我们通过Bellman方程框架将该策略纳入推荐系统的动态问题中。当与卖家自己的推荐相比，我们的系统产生了33%的利润增长。我们的反事实分析表明，浏览历史和过去的推荐在价值创造方面具有很强的互补作用。此外，有效地管理客户流失是价值创造的一个重要部分，而以前瞻性的方式推荐替代方案只会产生适度的效果。
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英文标题：
《No data? No problem! A Search-based Recommendation System with Cold
  Starts》
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作者：
Pedro M. Gardete, Carlos D. Santos
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
  Recommendation systems are essential ingredients in producing matches between products and buyers. Despite their ubiquity, they face two important challenges. First, they are data-intensive, a feature that precludes sophisticated recommendations by some types of sellers, including those selling durable goods. Second, they often focus on estimating fixed evaluations of products by consumers while ignoring state-dependent behaviors identified in the Marketing literature.   We propose a recommendation system based on consumer browsing behaviors, which bypasses the "cold start" problem described above, and takes into account the fact that consumers act as "moving targets," behaving differently depending on the recommendations suggested to them along their search journey. First, we recover the consumers' search policy function via machine learning methods. Second, we include that policy into the recommendation system's dynamic problem via a Bellman equation framework.   When compared with the seller's own recommendations, our system produces a profit increase of 33%. Our counterfactual analyses indicate that browsing history along with past recommendations feature strong complementary effects in value creation. Moreover, managing customer churn effectively is a big part of value creation, whereas recommending alternatives in a forward-looking way produces moderate effects.
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PDF链接：
https://arxiv.org/pdf/2010.03455