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ORCIDBarry Smyth
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- affiliation: University College Dublin, Insight Centre for Data Analytics, Ireland
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2020 – today
- 2024
[c318]Neil Hurley
, Erika Duriakova, James Geraci, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Barry Smyth, Aonghus Lawlor:
ALS Algorithm for Robust and Communication-Efficient Federated Learning. EuroMLSys@EuroSys 2024: 56-64- [c317]Diarmuid O'Reilly-Morgan, Elias Z. Tragos, James Geraci, Qinqin Wang, Neil Hurley, Barry Smyth, Aonghus Lawlor:
A Hybrid Decentralised Learning Topology for Recommendations with Improved Privacy. EuroMLSys@EuroSys 2024: 161-168 - [c316]Rian Dolphin, Barry Smyth, Ruihai Dong:
Contrastive Learning of Asset Embeddings from Financial Time Series. ICAIF 2024: 379-387 - [c315]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
A Case-Based Reasoning Approach to Post-injury Training Recommendations for Marathon Runners. ICCBR 2024: 338-353 - [c314]Heleen Muijlwijk, Martijn C. Willemsen, Barry Smyth, Wijnand A. IJsselsteijn:
Benefits of Human-AI Interaction for Expert Users Interacting with Prediction Models: a Study on Marathon Running. IUI 2024: 245-258 - [c313]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
Recommending Personalised Targeted Training Adjustments for Marathon Runners. RecSys 2024: 1051-1056 - [c312]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
Using Pseudo Cases and Stratified Case-Based Reasoning to Generate and Evaluate Training Adjustments for Marathon Runners. SGAI Conf. (2) 2024: 88-101 - [i22]Rian Dolphin, Barry Smyth, Ruihai Dong:
Contrastive Learning of Asset Embeddings from Financial Time Series. CoRR abs/2407.18645 (2024) - [i21]Padraig Cunningham, Barry Smyth:
An Analysis of the Impact of Gold Open Access Publications in Computer Science. CoRR abs/2408.10262 (2024) - 2023
- [j69]Nina Hagemann, Michael P. O'Mahony, Barry Smyth:
Visual Module Exploration: A Live-User Evaluation. Künstliche Intell. 37(2): 213-225 (2023) - [c311]Edoardo D'Amico, Khalil Muhammad, Elias Z. Tragos, Barry Smyth, Neil Hurley, Aonghus Lawlor:
Item Graph Convolution Collaborative Filtering for Inductive Recommendations. ECIR (1) 2023: 249-263 - [c310]Rian Dolphin, Barry Smyth, Ruihai Dong:
A Case-Based Reasoning Approach to Company Sector Classification Using a Novel Time-Series Case Representation. ICCBR 2023: 375-390 - [c309]Elias Z. Tragos, Diarmuid O'Reilly-Morgan, James Geraci, Bichen Shi, Barry Smyth, Cailbhe Doherty, Aonghus Lawlor, Neil Hurley:
Keeping People Active and Healthy at Home Using a Reinforcement Learning-based Fitness Recommendation Framework. IJCAI 2023: 6237-6245 - [c308]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
Modelling the Training Practices of Recreational Marathon Runners to Make Personalised Training Recommendations. UMAP 2023: 183-193 - [i20]Edoardo D'Amico, Khalil Muhammad, Elias Z. Tragos, Barry Smyth, Neil Hurley, Aonghus Lawlor:
Item Graph Convolution Collaborative Filtering for Inductive Recommendations. CoRR abs/2303.15946 (2023) - [i19]Rian Dolphin, Barry Smyth, Ruihai Dong:
Industry Classification Using a Novel Financial Time-Series Case Representation. CoRR abs/2305.00245 (2023) - [i18]Eoghan Cunningham, Derek Greene, Barry Smyth:
The Role of Document Embedding in Research Paper Recommender Systems: To Breakdown or to Bolster Disciplinary Borders? CoRR abs/2309.14984 (2023) - 2022
- [j68]Qinqin Wang, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Neil Hurley, Barry Smyth, Aonghus Lawlor, Ruihai Dong:
Learning Domain-Independent Representations via Shared Weight Auto-Encoder for Transfer Learning in Recommender Systems. IEEE Access 10: 71961-71972 (2022) - [j67]Eoghan Cunningham, Barry Smyth, Derek Greene:
Author multidisciplinarity and disciplinary roles in field of study networks. Appl. Netw. Sci. 7(1): 78 (2022) - [j66]Barry Smyth, Aonghus Lawlor, Jakim Berndsen, Ciara Feely:
Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runners. User Model. User Adapt. Interact. 32(5): 787-838 (2022) - [c307]Linyi Yang, Jiazheng Li, Ruihai Dong, Yue Zhang, Barry Smyth:
NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-Task Financial Forecasting. AAAI 2022: 11604-11612 - [c306]Rian Dolphin, Barry Smyth, Ruihai Dong:
A Machine Learning Approach to Industry Classification in Financial Markets. AICS 2022: 81-94 - [c305]Barry Smyth, Mark T. Keane:
A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations. ICCBR 2022: 18-32 - [c304]William Blanzeisky, Barry Smyth, Pádraig Cunningham:
Algorithmic Bias and Fairness in Case-Based Reasoning. ICCBR 2022: 48-62 - [c303]Greta Warren, Barry Smyth, Mark T. Keane:
"Better" Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfactuals Using Categorical Features for Explainable AI (XAI). ICCBR 2022: 63-78 - [c302]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
An Extended Case-Based Approach to Race-Time Prediction for Recreational Marathon Runners. ICCBR 2022: 335-349 - [c301]Qinqin Wang, Khalil Muhammad, Diarmuid O'Reilly-Morgan, Barry Smyth, Elias Z. Tragos, Aonghus Lawlor, Neil Hurley, Ruihai Dong:
MARF: User-Item Mutual Aware Representation with Feedback. ICWE 2022: 3-15 - [c300]Qinqin Wang, Elias Z. Tragos, Neil Hurley, Barry Smyth, Aonghus Lawlor, Ruihai Dong:
Entity-Enhanced Graph Convolutional Network for Accurate and Explainable Recommendation. UMAP 2022: 79-88 - [i17]Linyi Yang, Jiazheng Li, Ruihai Dong, Yue Zhang, Barry Smyth:
NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting. CoRR abs/2201.01770 (2022) - [i16]Rian Dolphin, Barry Smyth, Ruihai Dong:
Stock Embeddings: Learning Distributed Representations for Financial Assets. CoRR abs/2202.08968 (2022) - [i15]Eoghan Cunningham, Barry Smyth, Derek Greene:
Author Multidisciplinarity and Disciplinary Roles in Field of Study Networks. CoRR abs/2203.12504 (2022) - 2021
- [j65]Bichen Shi, Elias Z. Tragos, Makbule Gulcin Ozsoy, Ruihai Dong, Neil Hurley, Barry Smyth, Aonghus Lawlor:
DARES: An Asynchronous Distributed Recommender System Using Deep Reinforcement Learning. IEEE Access 9: 83340-83354 (2021) - [j64]Barry Smyth:
Estimating the Fatality Burden of SARS-CoV2. Digit. Gov. Res. Pract. 2(2): 21:1-21:8 (2021) - [j63]Barry Smyth:
Estimating Exposure Risk to Guide Behaviour During the SARS-COV2 Pandemic. Frontiers Digit. Health 3: 655745 (2021) - [j62]Mansura A. Khan, Barry Smyth, David Coyle:
Addressing the complexity of personalized, context-aware and health-aware food recommendations: an ensemble topic modelling based approach. J. Intell. Inf. Syst. 57(2): 229-269 (2021) - [c299]Linyi Yang, Jiazheng Li, Padraig Cunningham, Yue Zhang, Barry Smyth, Ruihai Dong:
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis. ACL/IJCNLP (1) 2021: 306-316 - [c298]Erika Duriakova, Elias Z. Tragos, Aonghus Lawlor, Barry Smyth, Neil Hurley:
Boosting the Training Time of Weakly Coordinated Distributed Machine Learning. IEEE BigData 2021: 1023-1029 - [c297]Eoghan Cunningham, Barry Smyth, Derek Greene:
Navigating Multidisciplinary Research Using Field of Study Networks. COMPLEX NETWORKS 2021: 104-115 - [c296]Rian Dolphin, Barry Smyth, Yang Xu, Ruihai Dong:
Measuring Financial Time Series Similarity with a View to Identifying Profitable Stock Market Opportunities. ICCBR 2021: 64-78 - [c295]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
A Case-Based Reasoning Approach to Predicting and Explaining Running Related Injuries. ICCBR 2021: 79-93 - [c294]Mohammed Temraz, Eoin M. Kenny, Elodie Ruelle, Laurence Shalloo, Barry Smyth, Mark T. Keane:
Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future. ICCBR 2021: 216-231 - [c293]Gechuan Zhang, Dairui Liu, Barry Smyth, Ruihai Dong:
Deciphering Ancient Chinese Oracle Bone Inscriptions Using Case-Based Reasoning. ICCBR 2021: 309-324 - [c292]Mark T. Keane, Eoin M. Kenny, Eoin Delaney, Barry Smyth:
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques. IJCAI 2021: 4466-4474 - [c291]Nina Hagemann, Michael P. O'Mahony, Barry Smyth:
A Live-User Evaluation of a Visual Module Recommender and Advisory System for Undergraduate Students. SGAI Conf. 2021: 299-312 - [i14]Barry Smyth, Mark T. Keane:
A Few Good Counterfactuals: Generating Interpretable, Plausible and Diverse Counterfactual Explanations. CoRR abs/2101.09056 (2021) - [i13]Mark T. Keane, Eoin M. Kenny, Eoin Delaney, Barry Smyth:
If Only We Had Better Counterfactual Explanations: Five Key Deficits to Rectify in the Evaluation of Counterfactual XAI Techniques. CoRR abs/2103.01035 (2021) - [i12]Mohammed Temraz, Eoin M. Kenny, Elodie Ruelle, Laurence Shalloo, Barry Smyth, Mark T. Keane:
Handling Climate Change Using Counterfactuals: Using Counterfactuals in Data Augmentation to Predict Crop Growth in an Uncertain Climate Future. CoRR abs/2104.04008 (2021) - [i11]Mark T. Keane, Eoin M. Kenny, Mohammed Temraz, Derek Greene, Barry Smyth:
Twin Systems for DeepCBR: A Menagerie of Deep Learning and Case-Based Reasoning Pairings for Explanation and Data Augmentation. CoRR abs/2104.14461 (2021) - [i10]Linyi Yang, Tin Lok James Ng, Barry Smyth, Ruihai Dong:
Fact Check: Analyzing Financial Events from Multilingual News Sources. CoRR abs/2106.15221 (2021) - [i9]Linyi Yang, Jiazheng Li, Pádraig Cunningham, Yue Zhang, Barry Smyth, Ruihai Dong:
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis. CoRR abs/2106.15231 (2021) - [i8]Rian Dolphin, Barry Smyth, Yang Xu, Ruihai Dong:
Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities. CoRR abs/2107.03926 (2021) - [i7]Eoghan Cunningham, Barry Smyth, Derek Greene:
Collaboration in the Time of COVID: A Scientometric Analysis of Multidisciplinary SARS-CoV-2 Research. CoRR abs/2108.13370 (2021) - [i6]Mansura A. Khan, Khalil Muhammad, Barry Smyth, David Coyle:
Investigating Health-Aware Smart-Nudging with Machine Learning to Help People Pursue Healthier Eating-Habits. CoRR abs/2110.07045 (2021) - 2020
- [j61]Makbule Gulcin Ozsoy, Diarmuid O'Reilly-Morgan, Panagiotis Symeonidis, Elias Z. Tragos, Neil Hurley, Barry Smyth, Aonghus Lawlor:
MP4Rec: Explainable and Accurate Top-N Recommendations in Heterogeneous Information Networks. IEEE Access 8: 181835-181847 (2020) - [j60]Barry Smyth:
Lockdowns & Rebounds: A Data Analysis of What Happens Next. Digit. Gov. Res. Pract. 1(4): 27:1-27:7 (2020) - [c290]Jiazheng Li, Linyi Yang, Barry Smyth, Ruihai Dong:
MAEC: A Multimodal Aligned Earnings Conference Call Dataset for Financial Risk Prediction. CIKM 2020: 3063-3070 - [c289]Linyi Yang, Eoin M. Kenny, Tin Lok James Ng, Yi Yang, Barry Smyth, Ruihai Dong:
Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification. COLING 2020: 6150-6160 - [c288]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
Using Case-Based Reasoning to Predict Marathon Performance and Recommend Tailored Training Plans. ICCBR 2020: 67-81 - [c287]Barry Smyth, Martijn C. Willemsen:
Predicting the Personal-Best Times of Speed Skaters Using Case-Based Reasoning. ICCBR 2020: 112-126 - [c286]Mark T. Keane, Barry Smyth:
Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI). ICCBR 2020: 163-178 - [c285]Jakim Berndsen, Barry Smyth, Aonghus Lawlor:
A Collaborative Filtering Approach to Successfully Completing The Marathon. ICMLA 2020: 653-658 - [c284]Khalil Muhammad, Qinqin Wang, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Barry Smyth, Neil Hurley, James Geraci, Aonghus Lawlor:
FedFast: Going Beyond Average for Faster Training of Federated Recommender Systems. KDD 2020: 1234-1242 - [c283]Jakim Berndsen, Barry Smyth, Aonghus Lawlor:
Mining Marathon Training Data to Generate Useful User Profiles. MLSA@PKDD/ECML 2020: 113-125 - [c282]Erika Duriakova, Weipéng Huáng, Elias Z. Tragos, Aonghus Lawlor, Barry Smyth, James Geraci, Neil Hurley:
An Algorithmic Framework for Decentralised Matrix Factorisation. ECML/PKDD (2) 2020: 307-323 - [c281]Francisco J. Peña, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Neil Hurley, Erika Duriakova, Barry Smyth, Aonghus Lawlor:
Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N Recommendation. RecSys 2020: 438-443 - [c280]Jakim Berndsen, Barry Smyth, Aonghus Lawlor:
Fit to Run: Personalised Recommendations for Marathon Training. RecSys 2020: 480-485 - [c279]Ciara Feely, Brian Caulfield, Aonghus Lawlor, Barry Smyth:
Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners. RecSys 2020: 539-544 - [c278]Linyi Yang, Tin Lok James Ng, Barry Smyth, Ruihai Dong:
HTML: Hierarchical Transformer-based Multi-task Learning for Volatility Prediction. WWW 2020: 441-451 - [i5]Mark T. Keane, Barry Smyth:
Good Counterfactuals and Where to Find Them: A Case-Based Technique for Generating Counterfactuals for Explainable AI (XAI). CoRR abs/2005.13997 (2020) - [i4]Linyi Yang, Eoin M. Kenny, Tin Lok James Ng, Yi Yang, Barry Smyth, Ruihai Dong:
Generating Plausible Counterfactual Explanations for Deep Transformers in Financial Text Classification. CoRR abs/2010.12512 (2020)
2010 – 2019
- 2019
- [c277]Barry Smyth:
Recommender Systems: A Healthy Obsession. AAAI 2019: 9790-9794 - [c276]Joeran Beel, Barry Smyth, Andrew Collins:
RARD II: The 94 Million Related-Article Recommendation Dataset. AMIR@ECIR 2019: 39-55 - [c275]Barry Smyth:
A Tale of Two Communities: An Analysis of Three Decades of Case-Based Reasoning Research. ICCBR 2019: 343-357 - [c274]Cathal McConnell, Barry Smyth:
Going Further with Cases: Using Case-Based Reasoning to Recommend Pacing Strategies for Ultra-Marathon Runners. ICCBR 2019: 358-372 - [c273]Nina Hagemann, Michael P. O'Mahony, Barry Smyth:
Visualising module dependencies in academic recommendations. IUI Companion 2019: 77-78 - [c272]Mansura A. Khan, Ellen Rushe, Barry Smyth, David Coyle:
Personalized, Health-Aware Recipe Recommendation: An Ensemble Topic Modeling Based Approach. HealthRecSys@RecSys 2019: 4-10 - [c271]Jakim Berndsen, Barry Smyth, Aonghus Lawlor:
Pace my race: recommendations for marathon running. RecSys 2019: 246-250 - [c270]Erika Duriakova, Elias Z. Tragos, Barry Smyth, Neil Hurley, Francisco J. Peña, Panagiotis Symeonidis, James Geraci, Aonghus Lawlor:
PDMFRec: a decentralised matrix factorisation with tunable user-centric privacy. RecSys 2019: 457-461 - [c269]Bichen Shi, Makbule Gulcin Ozsoy, Neil Hurley, Barry Smyth, Elias Z. Tragos, James Geraci, Aonghus Lawlor:
PyRecGym: a reinforcement learning gym for recommender systems. RecSys 2019: 491-495 - [i3]Mansura A. Khan, Ellen Rushe, Barry Smyth, David Coyle:
Personalized, Health-Aware Recipe Recommendation: An Ensemble Topic Modeling Based Approach. CoRR abs/1908.00148 (2019) - 2018
- [c268]Khalil Muhammad, Aonghus Lawlor, Barry Smyth:
A Multi-Domain Analysis of Explanation-Based Recommendation using User-Generated Reviews. FLAIRS 2018: 474-477 - [c267]Barry Smyth, Pádraig Cunningham:
An Analysis of Case Representations for Marathon Race Prediction and Planning. ICCBR 2018: 369-384 - [c266]Barry Smyth, Padraig Cunningham:
Marathon Race Planning: A Case-Based Reasoning Approach. IJCAI 2018: 5364-5368 - [c265]Nina Hagemann, Michael P. O'Mahony, Barry Smyth:
Module Advisor: Guiding Students with Recommendations. ITS 2018: 319-325 - [c264]Yichao Lu, Ruihai Dong, Barry Smyth:
Convolutional Matrix Factorization for Recommendation Explanation. IUI Companion 2018: 34:1-34:2 - [c263]Yichao Lu, Ruihai Dong, Barry Smyth:
Why I like it: multi-task learning for recommendation and explanation. RecSys 2018: 4-12 - [c262]Nina Hagemann, Michael P. O'Mahony, Barry Smyth:
Module advisor: a hybrid recommender system for elective module exploration. RecSys 2018: 498-499 - [c261]Nava Tintarev, Shahin Rostami, Barry Smyth:
Knowing the unknown: visualising consumption blind-spots in recommender systems. SAC 2018: 1396-1399 - [c260]Barry Smyth:
Running Recommendations: Personalisation Opportunities for Health and Fitness. UMAP 2018: 1 - [c259]Yichao Lu, Ruihai Dong, Barry Smyth:
Coevolutionary Recommendation Model: Mutual Learning between Ratings and Reviews. WWW 2018: 773-782 - [p9]Peter Brusilovsky, Barry Smyth, Bracha Shapira:
Social Search. Social Information Access 2018: 213-276 - [p8]Michael P. O'Mahony, Barry Smyth:
From Opinions to Recommendations. Social Information Access 2018: 480-509 - [i2]Jöran Beel, Barry Smyth, Andrew Collins:
RARD II: The 2nd Related-Article Recommendation Dataset. CoRR abs/1807.06918 (2018) - 2017
- [c258]Khalil Muhammad, Aonghus Lawlor, Barry Smyth:
On the Pros and Cons of Explanation-Based Ranking. ICCBR 2017: 227-241 - [c257]Barry Smyth, Pádraig Cunningham:
Running with Cases: A CBR Approach to Running Your Best Marathon. ICCBR 2017: 360-374 - [c256]Ruihai Dong, Barry Smyth:
User-Based Opinion-based Recommendation. IJCAI 2017: 4821-4825 - [c255]Severin Gsponer, Barry Smyth, Georgiana Ifrim:
Efficient Sequence Regression by Learning Linear Models in All-Subsequence Space. ECML/PKDD (2) 2017: 37-52 - [c254]Jakim Berndsen, Aonghus Lawlor, Barry Smyth:
Running with Recommendation. HealthRecSys@RecSys 2017: 18-21 - [c253]Barry Smyth, Pádraig Cunningham:
A Novel Recommender System for Helping Marathoners to Achieve a New Personal-Best. RecSys 2017: 116-120 - 2016
- [j59]David B. Leake, Barry Smyth, Rosina Weber:
Guest editors' introduction: special issue on case-based reasoning. J. Intell. Inf. Syst. 46(2): 235-236 (2016) - [j58]Ruihai Dong, Michael P. O'Mahony, Markus Schaal, Kevin McCarthy, Barry Smyth:
Combining similarity and sentiment in opinion mining for product recommendation. J. Intell. Inf. Syst. 46(2): 285-312 (2016) - [c252]Khalil Muhammad, Aonghus Lawlor, Barry Smyth:
Explanation-based Ranking in Opinionated Recommender Systems. AICS 2016: 128-139 - [c251]Khalil Muhammad, Aonghus Lawlor, Barry Smyth:
On the Use of Opinionated Explanations to Rank and Justify Recommendations. FLAIRS 2016: 554-559 - [c250]Ruihai Dong, Barry Smyth:
Personalized Opinion-Based Recommendation. ICCBR 2016: 93-107 - [c249]Khalil Ibrahim Muhammad, Aonghus Lawlor, Barry Smyth:
A Live-User Study of Opinionated Explanations for Recommender Systems. IUI 2016: 256-260 - [c248]Yichao Lu, Ruihai Dong, Barry Smyth:
Context-Aware Sentiment Detection from Ratings. SGAI Conf. 2016: 87-101 - [c247]Barry Smyth, Rachael Rafter, Sam Banks:
Harnessing Crowdsourced Recommendation Preference Data from Casual Gameplay. UMAP 2016: 95-104 - [c246]Ruihai Dong, Barry Smyth:
From More-Like-This to Better-Than-This: Hotel Recommendations from User Generated Reviews. UMAP 2016: 309-310 - 2015
- [c245]Barry Smyth, Rachael Rafter, Sam Banks:
A Game with a Purpose for Recommender Systems. HCOMP 2015: 38-39 - [c244]Barry Smyth:
From Small Sensors to Big Data. HT 2015: 101 - [c243]Khalil Muhammad, Aonghus Lawlor, Rachael Rafter, Barry Smyth:
Great Explanations: Opinionated Explanations for Recommendations. ICCBR 2015: 244-258 - [c242]Sam Banks, Rachael Rafter, Barry Smyth:
The Recommendation Game: Using a Game-with-a-Purpose to Generate Recommendation Data. RecSys 2015: 305-308 - [c241]Aonghus Lawlor, Khalil Muhammad, Rachael Rafter, Barry Smyth:
Opinionated Explanations for Recommendation Systems. SGAI Conf. 2015: 331-344 - [c240]Doychin Doychev, Rachael Rafter, Aonghus Lawlor, Barry Smyth:
News Recommenders: Real-Time, Real-Life Experiences. UMAP 2015: 337-342 - [c239]Khalil Muhammad, Aonghus Lawlor, Rachael Rafter, Barry Smyth:
Generating Personalised and Opinionated Review Summaries. UMAP Workshops 2015 - [p7]Ruihai Dong, Michael P. O'Mahony, Kevin McCarthy, Barry Smyth:
Case-Studies in Mining User-Generated Reviews for Recommendation. Advances in Social Media Analysis 2015: 105-127 - [r1]Barry Smyth, Maurice Coyle, Peter Briggs, Kevin McNally, Michael P. O'Mahony:
Collaboration, Reputation and Recommender Systems in Social Web Search. Recommender Systems Handbook 2015: 569-608 - 2014
- [j57]Kevin McNally, Michael P. O'Mahony, Barry Smyth:
A comparative study of collaboration-based reputation models for social recommender systems. User Model. User Adapt. Interact. 24(3): 219-260 (2014) - [c238]Ruihai Dong, Michael P. O'Mahony, Barry Smyth:
Further Experiments in Opinionated Product Recommendation. ICCBR 2014: 110-124 - 2013
- [j56]