Product feature recommendations are critical for online customers to purchase the right products based on the right features. For a customer, selecting the product that has the best trade-off between price and functionality is a time-consuming step in an online shopping experience, and customers can be overwhelmed by the available choices. However, determining the set of product features that most differentiate a particular product is still an open question in online recommender systems. In this paper, we focus on using interpretable machine learning methods to tackle this problem. First, we identify this unique product feature recommendation problem from a business perspective on a major US e-commerce site. Second, we formulate the problem into a price-driven supervised learning problem to discover the product features that could best explain the price of a product in a given product category. We build machine learning models with a model-agnostic method Shapley Values to understand the importance of each feature, rank and recommend the most essential features. Third, we leverage human experts to evaluate its relevancy. The results show that our method is superior to a strong baseline method based on customer behavior and significantly boosts the coverage by 45%. Finally, our proposed method shows comparable conversion rate against the baseline in online A/B tests.

中文翻译：

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可解释性机器学习的在线产品功能推荐

产品功能推荐对于在线客户根据正确的功能购买正确的产品至关重要。对于客户而言，选择在价格和功能之间取得最佳折衷的产品是在线购物体验中的一个耗时步骤，而可用的选择可能会使客户不知所措。但是，确定最能使特定产品脱颖而出的产品功能集仍然是在线推荐系统中的一个悬而未决的问题。在本文中，我们专注于使用可解释的机器学习方法来解决此问题。首先，我们从美国一家主要的电子商务网站的业务角度确定此独特的产品功能推荐问题。第二，我们将该问题公式化为价格驱动的有监督学习问题，以发现可以最好地解释给定产品类别中产品价格的产品功能。我们使用与模型无关的方法Shapley Values构建机器学习模型，以了解每个功能的重要性，对最重要的功能进行排名和推荐。第三，我们利用人类专家来评估其相关性。结果表明，我们的方法优于基于客户行为的强基准方法，并且覆盖率显着提高了45％。最后，我们提出的方法在在线A / B测试中显示出与基线相当的转化率。排名并推荐最基本的功能。第三，我们利用人类专家来评估其相关性。结果表明，我们的方法优于基于客户行为的强基准方法，并且覆盖率显着提高了45％。最后，我们提出的方法在在线A / B测试中显示出与基线相当的转化率。排名并推荐最基本的功能。第三，我们利用人类专家来评估其相关性。结果表明，我们的方法优于基于客户行为的强基准方法，并且覆盖率显着提高了45％。最后，我们提出的方法在在线A / B测试中显示出与基线相当的转化率。