11 papers
Edge-Level Automorphism in GNNs: A Quantitative Framework and Effective Designs For Link Prediction
Chen Shao, Donald Loveland, Tobias Käfer +1
Graph Neural Networks (GNNs) are effective for learning node and link embeddings through permutation-equivariant aggregation. However, standard GNNs collapse automorphic nodes, i.e…
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 +20
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…
Plain Transformers are Surprisingly Powerful Link Predictors
Quang Truong, Yu Song, Donald Loveland +4
Link prediction is a core challenge in graph machine learning, demanding models that capture rich and complex topological dependencies. While Graph Neural Networks (GNNs) are the s…
Understanding ID-Text Complementarity in Sequential Recommendation
Liam Collins, Bhuvesh Kumar, Clark Mingxuan Ju +4
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 repla…
Glance for Context: Learning When to Leverage LLMs for Node-Aware GNN-LLM Fusion
Donald Loveland, Yao-An Yang, Danai Koutra
Learning on text-attributed graphs has motivated the use of Large Language Models (LLMs) for graph learning. However, most fusion strategies are applied uniformly across all nodes…
On the Role of Weight Decay in Collaborative Filtering: A Popularity Perspective
Donald Loveland, Mingxuan Ju, Tong Zhao +2
Collaborative filtering (CF) enables large-scale recommendation systems by encoding information from historical user-item interactions into dense ID-embedding tables. However, as e…
GraphHash: Graph Clustering Enables Parameter Efficiency in Recommender Systems
Xinyi Wu, Donald Loveland, Runjin Chen +7
Deep recommender systems rely heavily on large embedding tables to handle high-cardinality categorical features such as user/item identifiers, and face significant memory constrain…