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.

翻译：在线零售商通常向客户提供大量可供筛选和浏览的产品选择。产品在列表中的排序方式取决于在线商店采用的排序算法。当前最先进的排序方法复杂且依赖多种信息，例如用户查询与意图、产品属性、流行度、时效性、评论或购买记录。然而，利用点击数据、用户评分或评论等用户生成数据的方法会使得尚未获得客户评分的新产品处于劣势。为此，我们提出了一种基于用户需求驱动的排序方法（UNDR），通过利用分面流行度和分面值流行度来反映明确的客户需求。作为一种以用户为中心的方法，该方法不依赖购买后的评分或评论，在规避冷启动问题的同时仍能反映普通用户的需求。通过两项初步的用户研究，我们将所提方法与基于评分排序的基线方法进行对比。研究结果表明，相较于基线方法，我们提出的方法能够生成更贴合当前用户需求的排序结果，但细粒度按使用场景的特定排序并未进一步优化排序效果。