# Neural IR Meets Graph Embedding: A Ranking Model for Product Search

arXiv: https://arxiv.org/abs/1901.08286 · HTML: https://mlsanity.com/paper/1901.08286/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jan 2019.

## Built on by (most cited first, 11 of 11)

- [Graph-based Multilingual Product Retrieval in E-commerce Search](https://mlsanity.com/paper/2105.02978/) (2021)
- [Learning to Ask: Conversational Product Search via Representation Learning](https://mlsanity.com/paper/2411.14466/) (2024)
- [AgenticShop: Benchmarking Agentic Product Curation for Personalized Web Shopping](https://mlsanity.com/paper/2602.12315/) (2026)
- [Unified Embedding Based Personalized Retrieval in Etsy Search](https://mlsanity.com/paper/2306.04833/) (2023)
- [Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search](https://mlsanity.com/paper/2505.14156/) (2025)
- [GARCIA: Powering Representations of Long-tail Query with Multi-granularity Contrastive Learning](https://mlsanity.com/paper/2304.12537/) (2023)
- [Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening](https://mlsanity.com/paper/2505.14033/) (2025)
- [Spatial Autoregressive Coding for Graph Neural Recommendation](https://mlsanity.com/paper/2205.09489/) (2022)
- [TraffNet: Learning Causality of Traffic Generation for What-if Prediction](https://mlsanity.com/paper/2303.15954/) (2023)
- [Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach](https://mlsanity.com/paper/2404.15354/) (2024)
- [Learning Large-scale Network Embedding from Representative Subgraph](https://mlsanity.com/paper/2112.01442/) (2021)

## Abstract (arXiv)

Recently, neural models for information retrieval are becoming increasingly popular. They provide effective approaches for product search due to their competitive advantages in semantic matching. However, it is challenging to use graph-based features, though proved very useful in IR literature, in these neural approaches. In this paper, we leverage the recent advances in graph embedding techniques to enable neural retrieval models to exploit graph-structured data for automatic feature extraction. The proposed approach can not only help to overcome the long-tail problem of click-through data, but also incorporate external heterogeneous information to improve search results. Extensive experiments on a real-world e-commerce dataset demonstrate significant improvement achieved by our proposed approach over multiple strong baselines both as an individual retrieval model and as a feature used in learning-to-rank frameworks.