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
A novel quantitative framework to assess GNN expressiveness for link prediction is introduced, and the edge automorphism ratio (EAR) is introduced, a scalar metric that quantifies a GNN's ability to distinguish links in a given graph.
Shao-Wei Chen, Donald Loveland, Tobias Käfer et al.· 0 citations
Large language models (LLMs) have shown promise for training-free recommendation, but LLM-generated user interests are often too broad for fine-grained item retrieval. Existing methods incorporate collaborative filtering (CF) signals in a pre-LLM manner through candidate reranking or prompt augmentation, yielding limit...
Kyungho Kim, Sunwoo Kim, Geon Lee et al.· 0 citations
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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