# Large Scale Product Graph Construction for Recommendation in E-commerce

arXiv: https://arxiv.org/abs/2010.05525 · HTML: https://mlsanity.com/paper/2010.05525/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 28 of 28)

- [Multi-behavior Self-supervised Learning for Recommendation](https://mlsanity.com/paper/2305.18238/) (2023)
- [Generative Recommendation for Large-Scale Advertising](https://mlsanity.com/paper/2602.22732/) (2026)
- [KuaiFormer: Transformer-Based Retrieval at Kuaishou](https://mlsanity.com/paper/2411.10057/) (2024)
- [Stop Treating Collisions Equally: Qualification-Aware Semantic ID Learning for Recommendation at Industrial Scale](https://mlsanity.com/paper/2603.00632/) (2026)
- [TRAWL: External Knowledge-Enhanced Recommendation with LLM Assistance](https://mlsanity.com/paper/2403.06642/) (2024)
- [TBGRecall: A Generative Retrieval Model for E-commerce Recommendation Scenarios](https://mlsanity.com/paper/2508.11977/) (2025)
- [GIFT: Graph-guIded Feature Transfer for Cold-Start Video Click-Through Rate Prediction](https://mlsanity.com/paper/2202.11525/) (2022)
- [LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in Shopee's Advertisement Recommendation](https://mlsanity.com/paper/2310.19394/) (2023)
- [MMBee: Live Streaming Gift-Sending Recommendations via Multi-Modal Fusion and Behaviour Expansion](https://mlsanity.com/paper/2407.00056/) (2024)
- [Cognitive-Aligned Spatio-Temporal Large Language Models For Next Point-of-Interest Prediction](https://mlsanity.com/paper/2510.14702/) (2025)
- [QARM V2: Quantitative Alignment Multi-Modal Recommendation for Reasoning User Sequence Modeling](https://mlsanity.com/paper/2602.08559/) (2026)
- [CRM: Retrieval Model with Controllable Condition](https://mlsanity.com/paper/2412.13844/) (2024)
- [GREAT: Guiding Query Generation with a Trie for Recommending Related Search about Video at Kuaishou](https://mlsanity.com/paper/2507.15267/) (2025)
- [A Metric for MLLM Alignment in Large-scale Recommendation](https://mlsanity.com/paper/2508.04963/) (2025)
- [LLM as Graph Kernel: Rethinking Message Passing on Text-Rich Graphs](https://mlsanity.com/paper/2603.14937/) (2026)
- [QueryAgent-R1: Bridging Query Generation and Product Retrieval for E-Commerce Query Recommendation](https://mlsanity.com/paper/2606.05671/) (2026)
- [Methodologies for Improving Modern Industrial Recommender Systems](https://mlsanity.com/paper/2308.01204/) (2023)
- [Circle Feature Graphormer: Can Circle Features Stimulate Graph Transformer?](https://mlsanity.com/paper/2309.06574/) (2023)
- [Deep Uncertainty-Based Explore for Index Construction and Retrieval in Recommendation System](https://mlsanity.com/paper/2408.00799/) (2024)
- [Revisiting Graph Projections for Effective Complementary Product Recommendation](https://mlsanity.com/paper/2506.09209/) (2025)
- [E2E-GRec: An End-to-End Joint Training Framework for Graph Neural Networks and Recommender Systems](https://mlsanity.com/paper/2511.20564/) (2025)
- [ReaSeq: Unleashing World Knowledge via Reasoning for Sequential Modeling](https://mlsanity.com/paper/2512.21257/) (2025)
- [LLM-I2I: Boost Your Small Item2Item Recommendation Model with Large Language Model](https://mlsanity.com/paper/2512.21595/) (2025)
- [CoNRec: Context-Discerning Negative Recommendation with LLMs](https://mlsanity.com/paper/2601.15721/) (2026)
- [Awakening Dormant Users: Generative Recommendation with Counterfactual Functional Role Reasoning](https://mlsanity.com/paper/2602.13134/) (2026)
- [NEXT: Reasoning-Driven Video Recommendation via a Vision-Language Model](https://mlsanity.com/paper/2607.24789/) (2026)
- [MARS: Multi-Agent Re-ranking for Repeat-Order Food Delivery Recommendation](https://mlsanity.com/paper/2607.25420/) (2026)
- [Latent-Aligned Reasoning for Multimodal Recommendation](https://mlsanity.com/paper/2609.04645/) (2026)

## Abstract (arXiv)

Building a recommendation system that serves billions of users on daily basis is a challenging problem, as the system needs to make astronomical number of predictions per second based on real-time user behaviors with O(1) time complexity. Such kind of large scale recommendation systems usually rely heavily on pre-built index of products to speedup the recommendation service so that online user waiting time is un-noticeable. One important indexing structure is the product-product index, where one can retrieval a list of ranked products given a seed product. The index can be viewed as a weighted product-product graph. In this paper, we present our novel technologies to efficiently build such kind of indexed product graphs. In particular, we propose the Swing algorithm to capture the substitute relationships between products, which can utilize the substructures of user-item click bi-partitive graph. Then we propose the Surprise algorithm for the modeling of complementary product relationships, which utilizes product category information and solves the sparsity problem of user co-purchasing graph via clustering technique. Base on these two approaches, we can build the basis product graph for recommendation in Taobao. The approaches are evaluated comprehensively with both offline and online experiments, and the results demonstrate the effectiveness and efficiency of the work.