A Llama-based model trained on serialized user stories unifies item, carousel, and search ranking and outperforms specialist baselines offline while improving some online metrics and reducing latency.
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A weighted similarity ensemble unifies user-item and item-item collaborative filtering using shared embeddings to deliver competitive top-N recommendations without extra fine-tuning.
citing papers explorer
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TubiFM: Unified Item, Carousel, and Search Ranking for Streaming Discovery
A Llama-based model trained on serialized user stories unifies item, carousel, and search ranking and outperforms specialist baselines offline while improving some online metrics and reducing latency.
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Collaborative Filtering Through Weighted Similarities of User and Item Embeddings
A weighted similarity ensemble unifies user-item and item-item collaborative filtering using shared embeddings to deliver competitive top-N recommendations without extra fine-tuning.