[Summary] Unifying Generative and Dense Retrieval for Sequential Recommendation
TL;DR Traditional item retrieval methods use user and item embeddings to predict relevance via inner product computation, which is not scalable for large systems. Generative models predict item indices directly but struggle with new items. This work proposes a hybrid model that combines item positions, text representations, and semantic IDs to predict both the next item embedding and several possible next item IDs. Then only this item subset along the new items are in the inner product with user representations....