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2020 – today
- 2022
[r2]Pablo Castells, Neil Hurley, Saúl Vargas:
Novelty and Diversity in Recommender Systems. Recommender Systems Handbook 2022: 603-646
2010 – 2019
- 2019
- [c17]Kallirroi Dogani, Matteo Tomassetti, Saúl Vargas, Benjamin Paul Chamberlain, Sofie De Cnudde:
Learning Embeddings for Product Size Recommendations. eCOM@SIGIR 2019 - [i2]Benjamin Paul Chamberlain, Stephen R. Hardwick, David R. Wardrope, Fabon Dzogang, Fabio Daolio, Saúl Vargas:
Scalable Hyperbolic Recommender Systems. CoRR abs/1902.08648 (2019) - 2018
- [c16]Ângelo Cardoso, Fabio Daolio, Saúl Vargas:
Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products. KDD 2018: 80-89 - [p1]Maya Hristakeva, Daniel Kershaw, Benjamin Pettit, Saúl Vargas, Kris Jack:
Academic Recommendations: The Mendeley Case. Collaborative Recommendations 2018: 599-625 - [i1]Ângelo Cardoso, Fabio Daolio, Saúl Vargas:
Product Characterisation towards Personalisation: Learning Attributes from Unstructured Data to Recommend Fashion Products. CoRR abs/1803.07679 (2018) - 2017
- [c15]Marco Rossetti, Saúl Vargas, Davide Magatti, Benjamin Pettit, Daniel Kershaw
, Maya Hristakeva, Kris Jack:
Effectively identifying users' research interests for scholarly reference management and discovery. SWM@WSDM 2017: 17-24 - [c14]Maya Hristakeva, Daniel Kershaw, Marco Rossetti, Petr Knoth, Benjamin Pettit, Saúl Vargas, Kris Jack:
Building recommender systems for scholarly information. SWM@WSDM 2017: 25-32 - 2016
- [c13]Saul Vargas, Richard McCreadie, Craig MacDonald, Iadh Ounis:
Comparing Overall and Targeted Sentiments in Social Media during Crises. ICWSM 2016: 695-698 - [c12]Saúl Vargas, Maya Hristakeva, Kris Jack:
Mendeley: Recommendations for Researchers. RecSys 2016: 365 - [c11]Saúl Vargas, Roi Blanco, Peter Mika:
Term-by-Term Query Auto-Completion for Mobile Search. WSDM 2016: 143-152 - 2015
- [c10]Saúl Vargas, Craig Macdonald, Iadh Ounis:
Analysing Compression Techniques for In-Memory Collaborative Filtering. RecSys Posters 2015 - [c9]Richard McCreadie, Saul Vargas, Craig MacDonald, Iadh Ounis, Stuart Mackie, Jarana Manotumruksa, Graham McDonald:
University of Glasgow at TREC 2015: Experiments with Terrier in Contextual Suggestion, Temporal Summarisation and Dynamic Domain Tracks. TREC 2015 - [r1]Pablo Castells, Neil J. Hurley, Saul Vargas:
Novelty and Diversity in Recommender Systems. Recommender Systems Handbook 2015: 881-918 - 2014
- [c8]Saul Vargas, Pablo Castells:
Improving sales diversity by recommending users to items. RecSys 2014: 145-152 - [c7]Saúl Vargas, Linas Baltrunas, Alexandros Karatzoglou, Pablo Castells:
Coverage, redundancy and size-awareness in genre diversity for recommender systems. RecSys 2014: 209-216 - [c6]Saúl Vargas:
Novelty and diversity enhancement and evaluation in recommender systems and information retrieval. SIGIR 2014: 1281 - 2013
- [c5]Saul Vargas, Rodrygo L. T. Santos, Craig Macdonald, Iadh Ounis:
Selecting effective expansion terms for diversity. OAIR 2013: 69-76 - [c4]Saul Vargas, Pablo Castells:
Exploiting the diversity of user preferences for recommendation. OAIR 2013: 129-136 - 2012
- [c3]Saul Vargas, Pablo Castells, David Vallet:
Explicit relevance models in intent-oriented information retrieval diversification. SIGIR 2012: 75-84 - 2011
- [c2]Saul Vargas, Pablo Castells:
Rank and relevance in novelty and diversity metrics for recommender systems. RecSys 2011: 109-116 - [c1]Saul Vargas, Pablo Castells, David Vallet:
Intent-oriented diversity in recommender systems. SIGIR 2011: 1211-1212
Coauthor Index
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