Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search

TL;DR

This paper introduces a global optimization framework for mutual influence aware ranking in e-commerce search, directly optimizing GMV and incorporating mutual influences into purchase probability estimation using RNNs and beam search.

Contribution

The paper proposes a novel framework that models mutual influences in e-commerce ranking, optimizing GMV directly and using RNNs and beam search for improved ranking.

Findings

Achieved a 5% increase in GMV in online A/B testing.

Introduced a global feature extension method for mutual influence modeling.

Utilized RNNs and beam search to optimize ranking order.

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.