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
Recommender systems are essential to real-world online services due to their substantial business value. Increasingly, large language models (LLMs) have been adopted for recommendation tasks, owing to their strong reasoning capabilities and their effectiveness in handling cold-start items. A large body of work has soug...
Sunwoo Kim, Geon Lee, Kyungho Kim et al.· Proceedings of the 32nd ACM...· 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.
This work proposes a simple, parameter-free intervention that initializes SID token embeddings directly from their corresponding centroids in the semantic embedding space, and shows that preserving SID geometry, beyond shared-prefix structure, provides a simple and effective semantic prior for LLM-based GR.
Donald Loveland, Liam Collins, B. Kumar et al.· 0 citations
The design and launch of SnapLGR, an LLM-based generative retrieval system for short-video recommendation at Snapchat shows that successful production SnapLGR requires joint design across representation learning, vocabulary grounding, and efficient training and serving.
Liam Collins, Jiwen Ren, Donald Loveland et al.· arXiv.org· 0 citations
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