Abstract
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.
Keywords
- Machine Learning Applications: Applications of Supervised Learning
- Multidisciplinary Topics and Applications: Information Retrieval
- Uncertainty in AI: Sequential Decision Making
- Machine Learning Applications: Big data ; Scalability
