Session-based Complementary Fashion Recommendations

TL;DR

This paper introduces ZSF-c, a session-based personalized recommendation algorithm for fashion e-commerce that improves the accuracy of suggesting complementary items, demonstrated by significant offline and online performance gains.

Contribution

The paper presents a novel session-based complementary recommendation algorithm with a new sampling strategy, tailored for fashion e-commerce, outperforming traditional collaborative filtering methods.

Findings

+8.2% Orders Recall@5 offline improvement

+3.24% increase in purchased products online

Significant accuracy enhancement over collaborative filtering

Abstract

In modern fashion e-commerce platforms, where customers can browse thousands to millions of products, recommender systems are useful tools to navigate and narrow down the vast assortment. In this scenario, complementary recommendations serve the user need to find items that can be worn together. In this paper, we present a personalized, session-based complementary item recommendation algorithm, ZSF-c, tailored for the fashion usecase. We propose a sampling strategy adopted to build the training set, which is useful when existing user interaction data cannot be directly used due to poor quality or availability. Our proposed approach shows significant improvements in terms of accuracy compared to the collaborative filtering approach, serving complementary item recommendations to our customers at the time of the experiments CF-c. The results show an offline relative uplift of +8.2% in Orders Recall@5, as well as a significant +3.24% increase in the number of purchased products measured in an online A/B test carried out in a fashion e-commerce platform with 28 million active customers.