In web search, mutual influences between documents have been studied from the
perspective of search result diversification. But the methods in web search is
not directly applicable to e-commerce search because of their differences. And
little research has been done on the mutual influences between items in
e-commerce search. We propose a global optimization framework for mutual
influence aware ranking in e-commerce search. Our framework directly optimizes
the Gross Merchandise Volume (GMV) for ranking, and decomposes ranking into two
tasks. The first task is mutual influence aware purchase probability
estimation. We propose a global feature extension method to incorporate mutual
influences into the features of an item. We also use Recurrent Neural Network
(RNN) to capture influences related to ranking orders in purchase probability
estimation. The second task is to find the best ranking order based on the
purchase probability estimations. We treat the second task as a sequence
generation problem and solved it using the beam search algorithm. We performed
online A/B test on a large e-commerce search engine. The results show that our
method brings a 5% increase in GMV for the search engine over a strong
baseline.

提出全局优化框架，实现对电子商务搜索中商品的排名，具体包括了相互影响的采购概率预测和排名任务，得出有关 GMV 的优化结果。

电商搜索中基于全局优化互影响意识的排名

Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search

The article retraces major events and milestones in the mutual influences
between mathematical logic and computer science since the 1950s.

该文章回顾了自 1950 年以来数学逻辑和计算机科学之间相互影响的重大事件和里程碑。