  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.
