9 papers
Training-Free LLM-Based Recommendation with Post-LLM Item Refinement Using Collaborative Signals
Kyungho Kim, Sunwoo Kim, Geon Lee +6
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 met…
LLM-Based Generative Retrieval for Snapchat Content Recommendation
Liam Collins, Jiwen Ren, Donald Loveland +19
Pretrained large language models (LLMs) are promising retrieval engines because they combine rich semantic priors, strong sequence modeling capabilities, and favorable scaling beha…
Preserving Item Semantics for Free: Rethinking Token Initialization in LLM-Based Generative Recommendation
Donald Loveland, Liam Collins, Bhuvesh Kumar +2
Recent advances in generative recommendation (GR) leverage large language models (LLMs) as recommender backbones, enabling LLMs to directly generate recommendations conditioned on…
Implicit Reasoning for Large Language Model-based Generative Recommendation
Yinhan He, Liam Collins, Bhuvesh Kumar +3
Large Language Models (LLMs) are increasingly adopted as backbones for Generative Recommendation (GR), promising access to pretrained world knowledge. Yet reliably invoking this kn…
On the Memorization Behavior of LLMs in Generative Recommendation: Observations, Implications, and Training Strategies
Sunwoo Kim, Sunkyung Lee, Clark Mingxuan Ju +5
Generative recommendation (GR) has emerged as a promising direction for recommender systems. Recently, large language models (LLMs) have been increasingly adopted for GR, as their…
Exploiting ID-Text Complementarity via Ensembling for Sequential Recommendation
Liam Collins, Bhuvesh Kumar, Clark Mingxuan Ju +4
Modern Sequential Recommendation (SR) models commonly utilize modality features to represent items, motivated in large part by recent advancements in language and vision modeling.…