| 2026 | WSDM | Transfer Learning via User-Item Graph Convolution for Enhanced Cross-Domain Recommendation. | Zheng Ju, Qinqin Wang, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Neil Hurley, Ruihai Dong, Aonghus Lawlor |
| 2025 | ECIR | DiffGR: A Discrete Diffusion-Based Model for Personalised Recommendation by Reconstructing User-Item Bipartite Graphs. | Zheng Ju, Honghui Du, Elias Z. Tragos, Neil Hurley, Aonghus Lawlor |
| 2025 | RecSys | SlateLLM: Distilling LLM Semantics into Session-Aware Slate Recommendation without Inference Overhead. | Aayush Singha Roy, Elias Z. Tragos, Aonghus Lawlor, Neil Hurley |
| 2025 | SIGIR | NodeRec+: A Lightweight Framework for Federated Recommender Systems. | Diarmuid O'Reilly-Morgan, Erika Duriakova, Elias Z. Tragos, Neil Hurley, Aonghus Lawlor |
| 2025 | VCIP | SplitFedEE: Balancing Communication and Accuracy. | Erika Duriakova, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Neil Hurley, Aonghus Lawlor |
| 2025 | UCC | Does Energy-efficiency Mean Carbon-awareness? A Critical Evaluation of Sustainable Serverless Edge Scheduling. | Shaoshu Zhu, Neil Hurley, Hadi Tabatabaee Malazi |
| 2024 | ADBIS | Entity Matching with Large Language Models as Weak and Strong Labellers. | Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Erika Duriakova, Honghui Du, Neil Hurley, Aonghus Lawlor |
| 2024 | CIKM | RecPrompt: A Self-tuning Prompting Framework for News Recommendation Using Large Language Models. | Dairui Liu, Boming Yang, Honghui Du, Derek Greene, Neil Hurley, Aonghus Lawlor, Ruihai Dong, Irene Li |
| 2024 | RecSys | Exploring Coresets for Efficient Training and Consistent Evaluation of Recommender Systems. | Zheng Ju, Honghui Du, Elias Z. Tragos, Neil Hurley, Aonghus Lawlor |
| 2023 | ECIR | Item Graph Convolution Collaborative Filtering for Inductive Recommendations. | Edoardo D'Amico, Khalil Muhammad, Elias Z. Tragos, Barry Smyth, Neil Hurley, Aonghus Lawlor |
| 2023 | FlAIRS | Addressing Fast Changing Fashion Trends in Multi-Stage Recommender Systems. | Aayush Singha Roy, Edoardo D'Amico, Aonghus Lawlor, Neil Hurley |
| 2023 | IJCAI | Keeping People Active and Healthy at Home Using a Reinforcement Learning-based Fitness Recommendation Framework. | Elias Z. Tragos, Diarmuid O'Reilly-Morgan, James Geraci, Bichen Shi, Barry Smyth, Cailbhe Doherty, Aonghus Lawlor, Neil Hurley |
| 2023 | PAKDD | Pure Spectral Graph Embeddings: Reinterpreting Graph Convolution for Top-N Recommendation. | Edoardo D'Amico, Aonghus Lawlor, Neil Hurley |
| 2023 | RecSys | Scalable Deep Q-Learning for Session-Based Slate Recommendation. | Aayush Singha Roy, Edoardo D'Amico, Elias Z. Tragos, Aonghus Lawlor, Neil Hurley |
| 2022 | ICWE | MARF: User-Item Mutual Aware Representation with Feedback. | Qinqin Wang, Khalil Muhammad, Diarmuid O'Reilly-Morgan, Barry Smyth, Elias Z. Tragos, Aonghus Lawlor, Neil Hurley, Ruihai Dong |
| 2020 | ACML | Partially Observable Markov Decision Process Modelling for Assessing Hierarchies. | Weipeng Huang, Guangyuan Piao, Ral Moreno, Neil Hurley |
| 2020 | KDD | FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems. | Khalil Muhammad, Qinqin Wang, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Barry Smyth, Neil Hurley, James Geraci, Aonghus Lawlor |
| 2020 | RecSys | Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation. | Francisco J. Pea, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Neil Hurley, Erika Duriakova, Barry Smyth, Aonghus Lawlor |
| 2019 | RecSys | PDMFRec: a decentralised matrix factorisation with tunable user-centric privacy. | Erika Duriakova, Elias Z. Tragos, Barry Smyth, Neil Hurley, Francisco J. Pea, Panagiotis Symeonidis, James Geraci, Aonghus Lawlor |
| 2019 | RecSys | PyRecGym: a reinforcement learning gym for recommender systems. | Bichen Shi, Makbule Gulcin Ozsoy, Neil Hurley, Barry Smyth, Elias Z. Tragos, James Geraci, Aonghus Lawlor |
| 2018 | ICPP | An Empirical Comparison of k-Shortest Simple Path Algorithms on Multicores. | Deepak Ajwani, Erika Duriakova, Neil Hurley, Ulrich Meyer, Alexander Schickedanz |
| 2018 | WSDM | Performance Analysis of a Privacy Constrained kNN Recommendation Using Data Sketches. | Armita Afsharinejad, Neil Hurley |
| 2017 | RecSys | How Diverse Is Your Audience? Exploring Consumer Diversity in Recommender Systems. | Jacek Wasilewski, Neil Hurley |
| 2016 | FlAIRS | Incorporating Diversity in a Learning to Rank Recommender System. | Jacek Wasilewski, Neil Hurley |
| 2016 | RecSys | Intent-Aware Diversification Using a Constrained PLSA. | Jacek Wasilewski, Neil Hurley |
| 2011 | RecSys | Towards Diverse Recommendation. | Neil Hurley |
| 2010 | ICTAI | Robust Collaborative Recommendation by Least Trimmed Squares Matrix Factorization. | Zunping Cheng, Neil Hurley |
| 2009 | IAAI | Trading Robustness for Privacy in Decentralized Recommender Systems. | Zunping Cheng, Neil Hurley |
| 2009 | ICTAI | Evaluating the Diversity of Top-N Recommendations. | Mi Zhang, Neil Hurley |
| 2009 | RecSys | Effective diverse and obfuscated attacks on model-based recommender systems. | Zunping Cheng, Neil Hurley |
| 2009 | RecSys | Statistical attack detection. | Neil Hurley, Zunping Cheng, Mi Zhang |
| 2008 | ICTAI | Analysis of Methods for Novel Case Selection. | Neil Hurley, Mi Zhang |
| 2008 | RecSys | Avoiding monotony: improving the diversity of recommendation lists. | Mi Zhang, Neil Hurley |
| 1995 | ICCBR | Evaluating the Application of CBR in Mesh Design for Simulation Problems. | Neil Hurley |
| 1995 | IJCAI | The Use of Fuzzy Representation in a CBR System for Mesh Design. | Neil Hurley |