Neural Multichannel Distant Speaker Diarization with Heavy-tailed Source Separation Model
本文针对远场说话人日志难题，提出了一种结合重尾模型的神经多通道方法，改进了原有基于高斯分布的模型，提高了日志错误率和Jaccard错误率。
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Music Playlist Captioning at Scale with Large Language Models
This work addresses the lack of interpretability in personalized playlists on music streaming platforms by deploying, for the first time at industrial scale, a large language model (LLM)-based automatic captioning system within Deezer’s Daily Mix recommendation service. The proposed approach integrates multi-source heterogeneous data and leverages a controllable generation mechanism to produce semantically rich and personalized natural language descriptions. Following deployment, the system yielded significant gains in user engagement, demonstrating that semantic explanations play a pivotal role in enhancing both the interpretability of recommendations and overall user experience. This study establishes an effective paradigm for controllable text generation with LLMs in real-world applications, offering practical insights into bridging the gap between algorithmic personalization and human-understandable justifications.
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Neural Multichannel Distant Speaker Diarization with Heavy-tailed Source Separation Model
本文针对远场说话人日志难题，提出了一种结合重尾模型的神经多通道方法，改进了原有基于高斯分布的模型，提高了日志错误率和Jaccard错误率。
0 citationsRead paper
Music Playlist Captioning at Scale with Large Language Models
This work addresses the lack of interpretability in personalized playlists on music streaming platforms by deploying, for the first time at industrial scale, a large language model (LLM)-based automatic captioning system within Deezer’s Daily Mix recommendation service. The proposed approach integrates multi-source heterogeneous data and leverages a controllable generation mechanism to produce semantically rich and personalized natural language descriptions. Following deployment, the system yielded significant gains in user engagement, demonstrating that semantic explanations play a pivotal role in enhancing both the interpretability of recommendations and overall user experience. This study establishes an effective paradigm for controllable text generation with LLMs in real-world applications, offering practical insights into bridging the gap between algorithmic personalization and human-understandable justifications.
0 citationsRead paper