Personalizing Incremental Video Search with Hybrid Text and ID Embeddings
Apple ML Research
Apple TV developed a personalized video search system combining text-based and ID-based embeddings to improve search ranking after each keystroke. The system achieved 8.63% improvement in NDCG@10 on ambiguous 1-3 character queries and 1.14% tap-through rate increase in online testing. The personalization approach delivers greater value for users with longer watch histories and on underspecified queries where intent is still forming.
Why it matters
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. Our approach learns two item embedding spaces: (i) a text-based multilingual encoder (TextEmb) fine-tuned on co-engagement triplets via contrastive learning, and (ii) an ID-based collaborative embedding model (IdEmb) trained on interaction-derived positives. At serving time, we construct user representations from…
