Dense retrieval-based Sequential Recommendation (SR) systems increasingly leverage text features to represent items, and generally do so in one of two ways: (i) by completely replacing ID embeddings with text embeddings, or (ii) by carefully combining ID and text embeddings through complex fusion mechanisms. While thes...
Liam Collins, B. Kumar, Ming-Xuan Ju et al.· Proceedings of the 20th ACM...· 0 citations
Deployed as a new retrieval source in the DPA production stack, SetMIR lifts overall CVR by 3.1%, while lifting CTR by 44% and CVR by 51% over the item-to-item retrieval source with the same item embeddings, ANN index, and retrieval quota.
Xiao-Dong Liu, Cong-Fei Zhang, Hsiang-Wei Chao et al.· 0 citations
This work introduces BONSAI : Branching-Optimized Node Structure for Adaptive Identifiers, a novel framework that co-designs textual term IDs and their underlying decoding trie that satisfies two desirable properties for a highly performant trie.
EGR is proposed, an Embedding-Native Generative Retrieval framework that uses a single shared LLM to learn item representations from item metadata and user representations from interaction histories in one embedding space, simplifying system design while improving retrieval quality and ad performance.
