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：在线零售商通常为客户提供大量产品选择,以便过滤和浏览。产品上市的顺序取决于在线商店采用的排名算法。最先进的排名方法十分复杂,借鉴了许多不同的信息,例如用户查询和意向、产品属性、受欢迎程度、耐用性、审查或购买等。然而,在两项初步用户研究中,我们比较了用户生成的数据,例如点击浏览数据、用户评级或审查尚未被客户评级的不利新产品。因此,我们建议采用用户-Needs-Driven评级(UNDR)方法,通过使用面部受欢迎度和面值受欢迎度来计算明确的客户需求。不过,作为一种不依赖购买后评级或审查的以用户为中心的方法,我们的方法避开了冷开始问题,同时仍然反映了普通客户的需求。在两项初步用户研究中,我们比较了我们的排名方法与基于评级的基线。我们的研究结果表明,我们提议的排名方法产生适合当前客户的排名,需要比基线要好得多。然而,更精细的用户特定用途排序没有进一步改进。
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