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One-step Reach: LLM-based Keyword Generation for Sponsored Search Advertising

Published: 13 May 2024 Publication History

Abstract

Query keyword matching plays a crucial role in sponsored search advertising by retrieving semantically related keywords of the user query to target relevant advertisements. Conventional technical solutions adopt the retrieve-judge-then-rank retrieval framework structured in cascade funnels. However, it has limitations in accurately depicting the semantic relevance between the query and keyword, and the cumulative funnel losses result in unsatisfactory precision and recall. To address the above issues, this paper proposes a Large Language Model (LLM)-based keyword generation method (LKG) to reach related keywords from the search query in one step. LKG models the query keyword matching as an end-to-end keyword generation task based on the LLM through multi-match prompt tuning. Moreover, it employs the feedback tuning and the prefix tree-based constrained beam search to improve the generation quality and efficiency. Extensive offline experiments and online A/B testing demonstrate the effectiveness and superiority of LKG which is fully deployed in the Baidu sponsored search system bringing significant improvements.

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References

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cover image ACM Conferences
WWW '24: Companion Proceedings of the ACM Web Conference 2024
May 2024
1928 pages
ISBN:9798400701726
DOI:10.1145/3589335
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 13 May 2024

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Author Tags

  1. keyword generation
  2. large language model
  3. sponsored search advertising

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WWW '24
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WWW '24: The ACM Web Conference 2024
May 13 - 17, 2024
Singapore, Singapore

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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