论文标题
没有数据？没问题！基于搜索的推荐系统，有冷的开始
No data? No problem! A Search-based Recommendation System with Cold Starts
论文作者
论文摘要
推荐系统是生产产品和买家之间匹配的重要成分。尽管它们无处不在，但他们面临两个重要的挑战。首先，它们是数据密集型的，该功能排除了某些类型的卖家（包括销售耐用商品的卖家）的精致建议。其次，他们通常专注于估计消费者对产品的固定评估，同时忽略了营销文献中确定的州依赖性行为。 我们提出了一个基于消费者浏览行为的推荐系统，该系统绕过上述“冷启动”问题，并考虑到消费者充当“移动目标”的事实，取决于在搜索过程中建议的建议。首先，我们通过机器学习方法恢复消费者的搜索策略功能。其次，我们通过Bellman方程框架将该策略包括在推荐系统的动态问题中。 与卖方自己的建议相比，我们的系统的利润增长了33％。我们的反事实分析表明，浏览历史记录以及过去的建议在价值创造中具有强烈的互补效应。此外，有效地管理客户流失是价值创造的重要组成部分，而以前瞻性方式推荐替代方案会产生适度的效果。
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.
