Go to ACL ARR 2025 February homepageBridging the Gap: Generative Retrieval via Query-to-Multi-Span Framework for Effective E-commerce Search
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 via query-to-multi-span. 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.
Paper Type: Long
Research Area: Information Retrieval and Text Mining
Research Area Keywords: generative models,dense retrieval
Contribution Types: Model analysis & interpretability, Approaches to low-resource settings
Languages Studied: chinese,english
Submission Number: 3833
Loading