Contextual Price Features for e-Commerce Search Ranking
Ishita Khan, Aritra Mandal, Prathyusha Senthil Kumar · 2019
Motivation The price of an item is one of the most useful attributes for e-commerce search. A prominent feature used by the machine learned ranker at eBay search measures, for each query, the deviance of the price of a candidate item to be ranked from the typical price distribution of the clicked items for that query. Like most e-commerce websites, in eBay search, users can restrict their search queries to certain specific categories, where category refers to nodes in the product catalog structure. As an example, a user could search for mens shirts and restrict results to either T-Shirts or Dress Shirts. In its most simple form, the price deviance feature is calculated in a category context agnostic fashion. However, the distribution from which the query price statistics (e.g. median, variance) are drawn varies significantly for different categories for the same query. For example, the query iphone x will have very different price distribution among clicked items in the Cell Phones Smartphones category as opposed to the Cell Phone Accessories category. The motivation of this work stems from accounting for category context for user queries while deriving and leveraging such distributional statistics. The core idea is extendable to other features used in the current search ranker besides item price, such as query-title similarity measure, and also to other contexts besides category, such as structured key value aspects. Problem Statement Given a query and a set of matching items, the machine learned search ranking model uses an array of diverse features to rank items. The problem addressed in this work is to enhance one such feature capturing item price deviance from query level price statistics to be sensitive to the category context of queries. Concretely, the ranker should be able to use different price distributions for the same query depending on the category context in which the query was issued. This problem has intriguing challenges in two major areas: first, since we have a hierarchical category tree structure, we need to figure out an effective way to propagate the price distribution across the tree; second, selecting the right statistic to measure the price deviation of candidate items from the typical price distribution for the query.