• A Summary of Netflix's Research on Cosine Similarity Unreliability in Semantic Embeddings

  • 2024/12/23
  • 再生時間： 7 分
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A Summary of Netflix's Research on Cosine Similarity Unreliability in Semantic Embeddings

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  • This episode analyzes the research paper titled "Is Cosine-Similarity of Embeddings Really About Similarity?" by Harald Steck, Chaitanya Ekanadham, and Nathan Kallus from Netflix Inc. and Cornell University, published on March 11, 2024. It examines the effectiveness of cosine similarity as a metric for assessing semantic similarity in high-dimensional embeddings, revealing limitations that arise from different regularization methods used in embedding models. The discussion explores how these regularization schemes can lead to unreliable or arbitrary similarity scores, challenging the conventional reliance on cosine similarity in applications such as language models and recommender systems. Additionally, the episode reviews the authors' proposed solutions, including training models with cosine similarity in mind and alternative data projection techniques, and presents their experimental findings that underscore the importance of critically evaluating similarity measures in machine learning practices.

    This podcast is created with the assistance of AI, the producers and editors take every effort to ensure each episode is of the highest quality and accuracy.

    For more information on content and research relating to this episode please see: https://arxiv.org/pdf/2403.05440
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あらすじ・解説

This episode analyzes the research paper titled "Is Cosine-Similarity of Embeddings Really About Similarity?" by Harald Steck, Chaitanya Ekanadham, and Nathan Kallus from Netflix Inc. and Cornell University, published on March 11, 2024. It examines the effectiveness of cosine similarity as a metric for assessing semantic similarity in high-dimensional embeddings, revealing limitations that arise from different regularization methods used in embedding models. The discussion explores how these regularization schemes can lead to unreliable or arbitrary similarity scores, challenging the conventional reliance on cosine similarity in applications such as language models and recommender systems. Additionally, the episode reviews the authors' proposed solutions, including training models with cosine similarity in mind and alternative data projection techniques, and presents their experimental findings that underscore the importance of critically evaluating similarity measures in machine learning practices.

This podcast is created with the assistance of AI, the producers and editors take every effort to ensure each episode is of the highest quality and accuracy.

For more information on content and research relating to this episode please see: https://arxiv.org/pdf/2403.05440
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