About us

“How lazily the sun goes down in Granada, it hides beneath the water, it conceals in the Alhambra!”
(Ernest Hemingway)

The Decision Support and Recommender Systems (DSRS) research group was founded in mid-2018 in the University of Bristol. Since March 2020, we moved to the DaSCI Andalusian Institute of Data Science and Artificial Intelligence: we are based in the University of Granada, in the beautiful namesake city of Granada, Spain. The University of Granada leads the QS World University Ranking, being the top university nationwide (Spain) in Computer Science & Artificial Intelligence research.

The DSRS Lab’s research focuses on investigating how data science, information fusion, decision analysis and artificial intelligence techniques can help supporting contemporary complex decision-making scenarios, involving human stakeholders, users, agents or groups of them. We are driven by developing cutting-edge solutions to nowadays societal problems involving challenging decision-making processes, through interdisciplinary, international and high-impact research and development activities.

Here are some examples of our research directions and ongoing projects:

  1. Large Group Decision Making and Consensus Building – Supporting collective decision-making scenarios requiring consensus among large and highly diverse groups of participants exhibiting conflicting opinions, attitudes, behaviour and social interactions with each other.
  2. Recommender Systems and Applications – Developing state-of-the-art personalisation approaches for recommending services, products or activities to both individuals and groups. Our target applications of interest include (i) personalisation for wellness and healthy living, (ii) tourism and leisure activity recommendation in smart cities, and (iii) reciprocal recommender systems for online dating and socialising-oriented personalisation.
  3. Intelligent Aggregation Techniques – Investigating how to intelligently incorporate and combine multiple sources of preferential, social and/or contextual data to (i) make highly informed group decisions and (ii) improve the performance of modern recommender system approaches.
  4. Human Decision Support with AI in the Loop – Supporting decision makers, stakeholders and analysts in making human-led data-driven decisions in complex scenarios, e.g. financial analysis, e-government, security analytics, management sciences, etc.