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Playlist Search Reinvented: LLMs Behind the Curtain

Published: 08 October 2024 Publication History

Abstract

Improving search functionality poses challenges such as data scarcity for model training, metadata enrichment for comprehensive document indexing, and the labor-intensive manual annotation for evaluation. Traditionally, iterative methods relying on human annotators and customer feedback have been used. However, recent advancements in Large Language Models (LLMs) offer new solutions. This paper focuses on applying LLMs to playlist search. Leveraging LLMs’ contextual understanding and generative capabilities automates metadata enrichment, reducing manual efforts and expediting training. LLMs also address data scarcity by generating synthetic training data and serve as scalable judges for evaluation, enhancing search performance assessment. We demonstrate how these innovations enhance playlist search, overcoming traditional limitations to improve search result accuracy and relevance.

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cover image ACM Conferences
RecSys '24: Proceedings of the 18th ACM Conference on Recommender Systems
October 2024
1438 pages
Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 08 October 2024

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Author Tags

  1. LLM augmentation
  2. MLOps
  3. Retrieval
  4. Semantic Search

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