UNDR: User-Needs-Driven Ranking of Products in E-Commerce

TL;DR

The paper introduces UNDR, a user-needs-driven ranking method for e-commerce that leverages facet popularity to improve product ranking without relying on user ratings, effectively addressing the cold-start problem.

Contribution

It proposes a novel ranking approach that uses explicit customer needs via facet popularity, bypassing reliance on user-generated ratings and reviews.

Findings

UNDR ranks products better according to customer needs than baseline methods.

User studies show significant improvement in ranking relevance.

Fine-grained usage-specific ranking did not enhance results.

Abstract

Online retailers often offer a vast choice of products to their customers to filter and browse through. The order in which the products are listed depends on the ranking algorithm employed in the online shop. State-of-the-art ranking methods are complex and draw on many different information, e.g., user query and intent, product attributes, popularity, recency, reviews, or purchases. However, approaches that incorporate user-generated data such as click-through data, user ratings, or reviews disadvantage new products that have not yet been rated by customers. We therefore propose the User-Needs-Driven Ranking (UNDR) method that accounts for explicit customer needs by using facet popularity and facet value popularity. As a user-centered approach that does not rely on post-purchase ratings or reviews, our method bypasses the cold-start problem while still reflecting the needs of an average customer. In two preliminary user studies, we compare our ranking method with a rating-based ranking baseline. Our findings show that our proposed approach generates a ranking that fits current customer needs significantly better than the baseline. However, a more fine-grained usage-specific ranking did not further improve the ranking.