论文标题
通过连续的重新级别来解决购买印象差距
Addressing Purchase-Impression Gap through a Sequential Re-ranker
论文作者
论文摘要
大型电子商务平台(例如eBay)携带各种库存,并为在线购物者提供多种购买选择。对于电子商务搜索引擎而言,至关重要的是,在最高的结果中展示可用的库存的多样性和选择,特别是在可能与搜索查询相关的各种购买意图的背景下。搜索排名者最常由学习到级别的模型提供支持,这些模型在培训过程中学习了项目之间的偏好。但是,他们在运行时分数独立于其他项目的项目。尽管通过这种评分功能将其放置在结果顶部的项目可能是独立的最佳选择,但它们可以作为一组次优。这可能会导致顶部结果中项目的理想分布与实际印象深刻的理想分布之间的不匹配。在本文中,我们提出了解决电子商务网站顶级搜索结果中观察到的购买印象差距的方法。我们基于历史购物方式建立了项目的理想分布。然后,我们提出了一个顺序的重读者,该序列可以有条不紊地重读由常规的点得分等级产生的顶级搜索结果。 Reranker通过依次选择候选人在独立相关性和潜力之间进行交易,从而产生重新排序的列表,该候选人通过利用特殊构造的功能来捕获已经添加到Reranked列表中的项目的印象分布,以解决购买印象差距。顺序reranker可以在多个项目方面解决购买印象差距。 Reranker的早期版本在eBay的转换和订婚指标中显示出有希望的升降机。基于对随机采样验证数据集的实验,我们观察到,呈现的重新疗法方法可在前20名结果中平均以平均为20%的购买印象差距降低了约10%的降低,同时改善了转换指标。
Large scale eCommerce platforms such as eBay carry a wide variety of inventory and provide several buying choices to online shoppers. It is critical for eCommerce search engines to showcase in the top results the variety and selection of inventory available, specifically in the context of the various buying intents that may be associated with a search query. Search rankers are most commonly powered by learning-to-rank models which learn the preference between items during training. However, they score items independent of other items at runtime. Although the items placed at top of the results by such scoring functions may be independently optimal, they can be sub-optimal as a set. This may lead to a mismatch between the ideal distribution of items in the top results vs what is actually impressed. In this paper, we present methods to address the purchase-impression gap observed in top search results on eCommerce sites. We establish the ideal distribution of items based on historic shopping patterns. We then present a sequential reranker that methodically reranks top search results produced by a conventional pointwise scoring ranker. The reranker produces a reordered list by sequentially selecting candidates trading off between their independent relevance and potential to address the purchase-impression gap by utilizing specially constructed features that capture impression distribution of items already added to a reranked list. The sequential reranker enables addressing purchase impression gap with respect to multiple item aspects. Early version of the reranker showed promising lifts in conversion and engagement metrics at eBay. Based on experiments on randomly sampled validation datasets, we observe that the reranking methodology presented produces around 10% reduction in purchase-impression gap at an average for the top 20 results, while making improvements to conversion metrics.