Online Product Feature Recommendations with Interpretable Machine Learning

TL;DR

This paper presents an interpretable machine learning approach using Shapley Values to recommend key product features that influence price, improving coverage and maintaining conversion rates in online shopping.

Contribution

It formulates feature recommendation as a price-driven supervised learning problem and demonstrates its effectiveness through online A/B testing and expert evaluation.

Findings

Boosts feature coverage by 45% over baseline

Maintains comparable conversion rates in A/B tests

Identifies important features using Shapley Values

Abstract

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.