Generative Retrieval with Preference Optimization for E-commerce Search

• URL: http://arxiv.org/abs/2407.19829v2
• Date: Fri, 25 Oct 2024 07:30:45 GMT
• Title: Generative Retrieval with Preference Optimization for E-commerce Search
• Authors: Mingming Li, Huimu Wang, Zuxu Chen, Guangtao Nie, Yiming Qiu, Guoyu Tang, Lin Liu, Jingwei Zhuo,
• Abstract summary: We develop an innovative framework for E-commerce search, called generative retrieval with preference optimization. We employ multi-span identifiers to represent raw item titles and transform the task of generating titles from queries into the task of generating multi-span identifiers from queries. Our 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.
• Score: 16.78829577915103
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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