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RetrievalFormer: Dual-Encoder Transformer for Cold-Item Recommendation

By PulseAugur Editorial · [2 sources] ·

Researchers have developed RetrievalFormer, a dual-encoder Transformer model designed for efficient approximate nearest neighbor retrieval and cold-item recommendation. This model addresses the challenge of incorporating new items into search and recommendation indexes without requiring retraining, a common issue with ID-softmax recommenders. RetrievalFormer maintains an open index by scoring new items based on features alone, achieving competitive performance on datasets like MovieLens-1M and MIND, particularly in cold-start scenarios. AI

IMPACT Introduces a novel approach to handling cold-start items in recommendation systems, potentially improving user experience and index efficiency.

RANK_REASON Research paper detailing a new model architecture and its performance on recommendation and retrieval tasks.

Read on arXiv cs.IR (Information Retrieval) →

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

RetrievalFormer: Dual-Encoder Transformer for Cold-Item Recommendation

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Research paper detailing a new model architecture and its performance on recommendation and retrieval tasks.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Theodore Rogers, Joe Standerfer, Dmitrii Timoshenko, Haoxue Li, Zuhaib Akhtar, Soyoung Yang ·

    RetrievalFormer: A Dual-Encoder Transformer for Efficient Approximate Nearest Neighbor Retrieval and Cold-Item Recommendation

    arXiv:2608.24079v1 Announce Type: cross Abstract: A shared search-and-recommendation index must score new items from features alone because search has no exploration slot. In a public log covering both surfaces over one catalog, $38.6\%$ of held-out query-search impressions show …

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Soyoung Yang ·

    RetrievalFormer: A Dual-Encoder Transformer for Efficient Approximate Nearest Neighbor Retrieval and Cold-Item Recommendation

    A shared search-and-recommendation index must score new items from features alone because search has no exploration slot. In a public log covering both surfaces over one catalog, $38.6\%$ of held-out query-search impressions show an item never previously shown or visited. For use…

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