Generative Retrieval with Preference Optimization for E-commerce Search
<|reference_start|>Generative Retrieval with Preference Optimization for E-commerce Search: Generative retrieval introduces a groundbreaking paradigm to document retrieval by directly generating the identifier of a pertinent document in response to a specific query. This paradigm has demonstrated considerable benefits and potential, particularly in representation and generalization capabilities, within the context of large language models. However, it faces significant challenges in E-commerce search scenarios, including the complexity of generating detailed item titles from brief queries, the presence of noise in item titles with weak language order, issues with long-tail queries, and the interpretability of results. To address these challenges, we have developed an innovative framework for E-commerce search, called generative retrieval with preference optimization. This framework is designed to effectively learn and align an autoregressive model with target data, subsequently generating the final item through constraint-based beam search. By employing multi-span identifiers to represent raw item titles and transforming the task of generating titles from queries into the task of generating multi-span identifiers from queries, we aim to simplify the generation process. The framework further aligns with human preferences using click data and employs a constrained search method to identify key spans for retrieving the final item, thereby enhancing result interpretability. Our extensive experiments show that this framework achieves competitive performance on a real-world dataset, and online A/B tests demonstrate the superiority and effectiveness in improving conversion gains.<|reference_end|>
@article{li2024generative,
    title={Generative Retrieval with Preference Optimization for E-commerce Search},
    author={Mingming Li, Huimu Wang, Zuxu Chen, Guangtao Nie, Yiming Qiu, Guoyu
  Tang, Lin Liu, Jingwei Zhuo},
    journal={arXiv preprint arXiv:2407.19829},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.19829},
    primaryClass={cs.IR cs.AI}
}