# An Efficient Embedding Based Ad Retrieval with GPU-Powered Feature Interaction

arXiv: https://arxiv.org/abs/2511.22460 · HTML: https://mlsanity.com/paper/2511.22460/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 27 Nov 2025.

## Builds on (most cited first, 13 of 13)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Self-Attentive Sequential Recommendation](https://mlsanity.com/paper/1808.09781/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [Wide & Deep Learning for Recommender Systems](https://mlsanity.com/paper/1606.07792/) (2016)
- [COLD: Towards the Next Generation of Pre-Ranking System](https://mlsanity.com/paper/2007.16122/) (2020)
- [Efficient Inverted Indexes for Approximate Retrieval over Learned Sparse Representations](https://mlsanity.com/paper/2404.18812/) (2024)
- [Approximate Nearest Neighbor Search under Neural Similarity Metric for Large-Scale Recommendation](https://mlsanity.com/paper/2202.10226/) (2022)
- [SLIM: Sparsified Late Interaction for Multi-Vector Retrieval with Inverted Indexes](https://mlsanity.com/paper/2302.06587/) (2023)
- [Locality-Sensitive Hashing Scheme based on Longest Circular Co-Substring](https://mlsanity.com/paper/2004.05345/) (2020)
- [Deep Retrieval: Learning A Retrievable Structure for Large-Scale Recommendations](https://mlsanity.com/paper/2007.07203/) (2020)
- [Enhancing Relevance of Embedding-based Retrieval at Walmart](https://mlsanity.com/paper/2408.04884/) (2024)

## Abstract (arXiv)

In large-scale advertising recommendation systems, retrieval serves as a critical component, aiming to efficiently select a subset of candidate ads relevant to user behaviors from a massive ad inventory for subsequent ranking and recommendation. The Embedding-Based Retrieval (EBR) methods modeled by the dual-tower network are widely used in the industry to maintain both retrieval efficiency and accuracy. However, the dual-tower model has significant limitations: the embeddings of users and ads interact only at the final inner product computation, resulting in insufficient feature interaction capabilities. Although DNN-based models with both user and ad as input features, allowing for early-stage interaction between these features, are introduced in the ranking stage to mitigate this issue, they are computationally infeasible for the retrieval stage. To bridge this gap, this paper proposes an efficient GPU-based feature interaction for the dual-tower network to significantly improve retrieval accuracy while substantially reducing computational costs. Specifically, we introduce a novel compressed inverted list designed for GPU acceleration, enabling efficient feature interaction computation at scale. To the best of our knowledge, this is the first framework in the industry to successfully implement Wide and Deep in a retrieval system. We apply this model to the real-world business scenarios in Tencent Advertising, and experimental results demonstrate that our method outperforms existing approaches in offline evaluation and has been successfully deployed to Tencent's advertising recommendation system, delivering significant online performance gains. This improvement not only validates the effectiveness of the proposed method, but also provides new practical guidance for optimizing large-scale ad retrieval systems.