2 citations · 3 across the 7 of their papers we have counts for
8 papers
Neither Black nor White: Balancing Semantic and Collaborative Signals with Graph-Informed Semantic IDs (GrIS)
Aleksei Medvedev, Alejandro Ariza-Casabona, Steven Derby +3
Existing work on Semantic IDs (SIDs) for generative recommendation treats SID construction as a representation learning problem: encode items into a quantised latent space and read…
Efficient Large-Scale Cross-Domain Sequential Recommendation with Dynamic State Representations
Manuel V. Loureiro, Steven Derby, Aleksei Medvedev +3
Recently, autoregressive recommendation models (ARMs), such as Meta's HSTU model, have emerged as a major breakthrough over traditional Deep Learning Recommendation Models (DLRMs),…
SPICED: News Similarity Detection Dataset with Multiple Topics and Complexity Levels
Elena Shushkevich, Long Mai, Manuel V. Loureiro +2
The proliferation of news media outlets has increased the demand for intelligent systems capable of detecting redundant information in news articles in order to enhance user experi…
STA: Self-controlled Text Augmentation for Improving Text Classifications
Congcong Wang, Gonzalo Fiz Pontiveros, Steven Derby +1
Despite recent advancements in Machine Learning, many tasks still involve working in low-data regimes which can make solving natural language problems difficult. Recently, a number…
Topics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural Networks
Manuel V. Loureiro, Steven Derby, Tri Kurniawan Wijaya
Topic models aim to reveal latent structures within a corpus of text, typically through the use of term-frequency statistics over bag-of-words representations from documents. In re…
Multilingual News Location Detection using an Entity-Based Siamese Network with Semi-Supervised Contrastive Learning and Knowledge Base
Víctor Suárez-Paniagua, Steven Derby, Tri Kurniawan Wijaya
Early detection of relevant locations in a piece of news is especially important in extreme events such as environmental disasters, war conflicts, disease outbreaks, or political t…
Feature2Vec: Distributional semantic modelling of human property knowledge
Steven Derby, Paul Miller, Barry Devereux
Feature norm datasets of human conceptual knowledge, collected in surveys of human volunteers, yield highly interpretable models of word meaning and play an important role in neuro…
Using Sparse Semantic Embeddings Learned from Multimodal Text and Image Data to Model Human Conceptual Knowledge
Steven Derby, Paul Miller, Brian Murphy +1
Distributional models provide a convenient way to model semantics using dense embedding spaces derived from unsupervised learning algorithms. However, the dimensions of dense embed…