Hierarchical Structured Neural Network for Retrieval
<|reference_start|>Hierarchical Structured Neural Network for Retrieval: Embedding Based Retrieval (EBR) is a crucial component of the retrieval stage in (Ads) Recommendation System that utilizes Two Tower or Siamese Networks to learn embeddings for both users and items (ads). It then employs an Approximate Nearest Neighbor Search (ANN) to efficiently retrieve the most relevant ads for a specific user. Despite the recent rise to popularity in the industry, they have a couple of limitations. Firstly, Two Tower model architecture uses a single dot product interaction which despite their efficiency fail to capture the data distribution in practice. Secondly, the centroid representation and cluster assignment, which are components of ANN, occur after the training process has been completed. As a result, they do not take into account the optimization criteria used for retrieval model. In this paper, we present Hierarchical Structured Neural Network (HSNN), a deployed jointly optimized hierarchical clustering and neural network model that can take advantage of sophisticated interactions and model architectures that are more common in the ranking stages while maintaining a sub-linear inference cost. We achieve 6.5% improvement in offline evaluation and also demonstrate 1.22% online gains through A/B experiments. HSNN has been successfully deployed into the Ads Recommendation system and is currently handling major portion of the traffic. The paper shares our experience in developing this system, dealing with challenges like freshness, volatility, cold start recommendations, cluster collapse and lessons deploying the model in a large scale retrieval production system.<|reference_end|>
@article{rangadurai2024hierarchical,
    title={Hierarchical Structured Neural Network for Retrieval},
    author={Kaushik Rangadurai, Siyang Yuan, Minhui Huang, Yiqun Liu, Golnaz
  Ghasemiesfeh, Yunchen Pu, Xinfeng Xie, Xingfeng He, Fangzhou Xu, Andrew Cui,
  Vidhoon Viswanathan, Yan Dong, Liang Xiong, Lin Yang, Liang Wang, Jiyan Yang,
  Chonglin Sun},
    journal={arXiv preprint arXiv:2408.06653},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06653},
    primaryClass={cs.IR cs.AI}
}