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Apple TV search system uses hybrid embeddings to boost personalization

By PulseAugur Editorial · [2 sources] ·

Apple's machine learning research team has developed a personalization system for Apple TV search that enhances incremental search results. The system combines text-based and ID-based embeddings, trained using contrastive learning and interaction data, respectively. When integrated into an XGBoost ranker, this hybrid approach significantly improves metrics like NDCG@10 and MRR, particularly for ambiguous, short-prefix queries and users with extensive watch histories. Online experiments confirmed these improvements with higher tap-through and conversion rates. AI

IMPACT Enhances user experience in video search by improving relevance and discoverability through personalized recommendations.

RANK_REASON Research paper detailing a new personalization system for video search.

Read on Apple Machine Learning Research →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Apple TV search system uses hybrid embeddings to boost personalization

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Research paper detailing a new personalization system for video search.
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2 independent sources
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product, paper, infra
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High
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69 days old
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COVERAGE [2]

  1. Apple Machine Learning Research TIER_1 English(EN) ·

    Personalizing Incremental Video Search with Hybrid Text and ID Embeddings

    Incremental video search requires high-quality ranking after each keystroke, where intent is often underspecified (e.g., 1–3 character prefixes). We present a personalization system for Apple TV search that combines complementary semantic and collaborative signals at ranking time…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Xuetao Yin ·

    Personalizing Incremental Video Search with Hybrid Text and ID Embeddings

    Incremental video search requires high-quality ranking after each keystroke, where intent is often underspecified (e.g., 1-3 character prefixes). We present a personalization system for Apple TV search that combines complementary semantic and collaborative signals at ranking time…

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