# Globally Optimized Mutual Influence Aware Ranking in E-Commerce Search

arXiv: https://arxiv.org/abs/1805.08524 · HTML: https://mlsanity.com/paper/1805.08524/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2018.

## Built on by (most cited first, 18 of 18)

- [Non-autoregressive Generative Models for Reranking Recommendation](https://mlsanity.com/paper/2402.06871/) (2024)
- [PEAR: Personalized Re-ranking with Contextualized Transformer for Recommendation](https://mlsanity.com/paper/2203.12267/) (2022)
- [Modeling Users' Contextualized Page-wise Feedback for Click-Through Rate Prediction in E-commerce Search](https://mlsanity.com/paper/2203.15542/) (2022)
- [PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce](https://mlsanity.com/paper/2302.03487/) (2023)
- [Enhancing Cross-Sectional Currency Strategies by Context-Aware Learning to Rank with Self-Attention](https://mlsanity.com/paper/2105.10019/) (2021)
- [NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized Recommendation Systems](https://mlsanity.com/paper/2502.06097/) (2025)
- [NR4DER: Neural Re-ranking for Diversified Exercise Recommendation](https://mlsanity.com/paper/2506.06341/) (2025)
- [Beyond Item Dissimilarities: Diversifying by Intent in Recommender Systems](https://mlsanity.com/paper/2405.12327/) (2024)
- [GReF: A Unified Generative Framework for Efficient Reranking via Ordered Multi-token Prediction](https://mlsanity.com/paper/2510.25220/) (2025)
- [Efficient Personalized Reranking with Semi-Autoregressive Generation and Online Knowledge Distillation](https://mlsanity.com/paper/2603.07107/) (2026)
- [Re-ranking With Constraints on Diversified Exposures for Homepage Recommender System](https://mlsanity.com/paper/2112.07621/) (2021)
- [Advancing Re-Ranking with Multimodal Fusion and Target-Oriented Auxiliary Tasks in E-Commerce Search](https://mlsanity.com/paper/2408.05751/) (2024)
- [JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System](https://mlsanity.com/paper/2207.13311/) (2022)
- [Towards Principled Learning for Re-ranking in Recommender Systems](https://mlsanity.com/paper/2504.04188/) (2025)
- [Reasoning While Recommending: Entropy-Guided Latent Reasoning in Generative Re-ranking Models](https://mlsanity.com/paper/2601.13533/) (2026)
- [Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems](https://mlsanity.com/paper/2311.06323/) (2023)
- [Do Not Wait: Learning Re-Ranking Model Without User Feedback At Serving Time in E-Commerce](https://mlsanity.com/paper/2406.14004/) (2024)
- [DeGRe: Dense-supervised Generative Reranking for Recommendation](https://mlsanity.com/paper/2605.25749/) (2026)

## Abstract (arXiv)

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.