UNIA Summer School: Data Science and Machine Learning with Applications

Members of DaSCI Andalusian Institute on Data Science and Computational Intelligence are organising a summer school with the International University of Andalucía (UNIA). The school, entitled “Data Science and Machine Learning with Applications” will be fully virtual (online), held 24-28th August and taught predominantly in Spanish (with some materials or talks in English).


More info and enrolment available here (info in Spanish).

The course, whose directors are Iván Palomares and Macarena Espinilla, includes the following instructors along 15 sessions, lasting a total 25 hours:

Dr. Iván Palomares Carrascosa (Universidad de Granada).

Dr. Francisco Herrera Triguero (Universidad de Granada).

D. Hugo Alcaraz-Herrera (Universidad de Bristol, Reino Unido).

Dra. Victoria López López (Universidad Complutense de Madrid).

Dra. Macarena Espinilla Estévez (Universidad de Jaén).

Dr. Javier Medina Quero (Universidad de Jaén).

Dr. Iván Cantador Gutiérrez (Universidad Autónoma de Madrid).

Dr. Alejandro Bellogín Kouki (Universidad Autónoma de Madrid).

D. Nicolas Kourtellis (Telefónica I+D).

ACM SIGIR 2020 Tutorial: Reciprocal Recommendation



The half-day Tutorial “Reciprocal Recommendation: matching users with the right users”, is part of the programme of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (ACM SIGIR’20) organised in Xi’an (China), 25-30th July 2020.


The Tutorial was held on 26th July 2020. Get a glimpse about this Tutorial aims and scope below. A full description of the tutorial can be found in its accepted Paper (published in ACM Proceedings).

Tutorial Materials:

  • Tutorial slides (PDF) (slideshare).
  • Supplementary paper (arXiv preprint): Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities on Social Recommendation. (Link)



Keep calm and move south: DSRS Lab joins DaSCI Institute in Granada, Spain!

Descubre la magia de la ciudad de Granada

Having very recently materialised the plans – sought long time ago – to move to my home place in Spain by joining the University of Granada, the DSRS Lab in Bristol will of course follow.

The lab is now becoming part of the Andalusian Institute of Data Science and Computational Intelligence (DaSCI), a multi-university institute based in southern Spain and led by Prof. Francisco Herrera. Personally, I couldn’t be happier about this positive step forward.

Although all these changes happened amid an unprecedented global health crisis worldwide, this doesn’t mean that both the Lab members and collaborators – both existing and new alike – and myself aren’t working hard on new projects and collaborations within the frame of this institute with international projection!

An important news is that The Current DSRS Lab Website will soon be migrated into a new address, which will be announced very soon, both here and in our Twitter account, as well as on my personal webpage (www.ivanpc.com)

I would like to thank sincerely those people in Bristol University and The Alan Turing Institute who did make it possible the foundation of this lab (back on summer 2018) with their valuable, unconditional support. Thanks to them, we are now starting a new chapter in the Lab’s life as part of a cornerstone research Institute, with the growth of our team and R&D projects in which we will be involved. We will substantially update this Website along the next days with some news on these new updates and projects!

Stay safe, and stay positive and happy 🙂

Iván P.C.

Large-Scale Decision Making Overview, published in Information Fusion

The research area of Large Group Decision Making (LGDM) has acquired growing importance in the last 5 to 6 six years, with many researchers around the globe developing models and solutions to support collective decisions at large scale.

A variant of this LGDM framework, called Large-Scale Decision Making is the subject of this article published in Information Fusion (Impact Factor: 10.716, ranked 3/134 by ISI-JCR in Computer Science: Artificial Intelligence), in which the DSRS Lab collaborated with scientists from University of Granada (Spain), Tianjin University (China) and Sichuan University (China).


The last decade witnessed tremendous developments in social media and e-democracy technologies. A funda- mental aspect in these paradigms is that the number of decision makers allowed to partake in a decision making event drastically increases. As a result Large Scale Decision Making (LSDM) has established itself as an emerging and rapidly developing research field, attracting comprehensive studies in the last decade. LSDM events are a complex class of decision making problems, in which multiple and highly diverse stakeholders are involved and the provided alternatives are assessed considering multiple criteria/attributes. Since some of the extant LSDM re- search was extended from group decision making scenarios, there is no established definition for a LSDM problem as of yet. We firstly propose a clear definition and characterization of LSDM events as a basis for characterizing this emerging family of decision frameworks. Secondly, a classification of LSDM literature is provided. Effectively solving an LSDM problem is usually a complex and challenging process, in which reaching a high consensus or accounting for the agreement or conflict relationships between participants becomes critical. Accordingly, we present a taxonomy and an overview of LSDM models, predicated on their key elements, i.e. the procedures and specific steps followed by the existing models: consensus measurement, subgroup clustering, behavior man- agement, and consensus building mechanisms. Finally, we provide a discussion in which we identify research challenges and propose future research directions under a triple perspective: key LSDM methodologies, AI and data fusion for LSDM, and innovative applications. The potential rise of AI-based LSDM is particularly highlighted in the discussion provided.

The article establishes a taxonomy around which existing LSDM literature is overviewed, based on the key elements/processes undertaken in LSDM models (e.g. consensus measurement and building, subgroup clustering, behavior management…) and the types of approaches adopted by different scholars for each of these key elements.

Fig. 7


Furthermore, the article provides a comprehensive discussion on the challenges and future research directions in LSDM, with special emphasis on (i) the potential role of AI and Data Fusion/Data Science technologies to improve these decision support models, and (ii) Innovative Areas of Application of LSDM.


Fig. 10

Article information:

Ru-Xi Ding, Iván Palomares, Xueqing Wang, Guo-Rui Yang, Bingsheng Liu, Yucheng Dong, Enrique Herrera-Viedma, Francisco Herrera. Large-Scale decision-making: Characterization, taxonomy, challenges and future directions from an Artificial Intelligence and applications perspective. Information Fusion. Volume 59, July 2020, Pages 84-102.

Seed-corn funding 2020 winners (Jean Golding Institute)

we are pleased to start the new year with great news.

Two DSRS lab members will participate alongside Dr. Zoi Toumpakari (Lecturer in Nutrition and Behaviour Change in Bristol), Daniele Quercia and Luca Maria Aiello  from King’s College London & Nokia Bell Labs, in a seed-corn funded project with the Jean Golding Institute: ‘Automating food aggregation for nutrition and health research’. 

Our contribution will be to explore algorithms at the intersection between classification (supervised machine learning) and multi-criteria decision analysis, for intelligent food data allocation.

Stay tuned for updates.

Source: JGI Blog

Off to ACM Recsys 2019

Resultado de imagen de copenhague tivoli

Three members of DSRS are finishing their packing to arrive at Copenhagen (yes, there are Chinese pagodas in Copenhagen!) this weekend when ACM Recsys 2019, the Premier Worldwide Conference on Recommender Systems, is about to start.

We are glad to have three accepted papers in the whole technical programme; (i) one on user-to-user reciprocal recommendations in online dating, (ii) one on recommending healthy meal-exercising bundles, and (iii) one on recommending restaurant deals to citizens and tourists alike:

If you would like to hear more about our work, please approach us, we will be very happy to chat.

Finally, as a side opportunity, if you are an experienced RecSys researcher or industry professional, you are based in (or geographically near) Spain (or you like it!), you like teaching/training, and you’d like to do some extra paid activity next year 2020, please speak to me (Iván Palomares) during the conference: I will be definitely interested in hearing from you!

1st International ‘Alan Turing’ Conference on Decision Support and Recommender Systems (DSRS-Turing 2019)

1st International ‘Alan Turing’ Conference on Decision Support and Recommender Systems (DSRS-Turing 2019)


London, UK. 21-22 November 2019

Decision Making processes constantly occur in our daily lives, both at an individual, collective and large-scale societal level. Even for machines and AI systems, making autonomous and rational decisions has nowadays become part of their core duties. Decision making often occurs amid highly dynamic, uncertain and data-pervaded scenarios, thereby becoming increasingly complex. This conference based in the UK’s Alan Turing Institute brings together international researchers, industry professionals and domain experts to discuss latest trends and ongoing challenges in:

  • Human and AI-driven complex decision making, e.g. group and multi-criteria decision-making.
  • Role of Machine Learning and Data-Driven AI techniques in supporting decisions.
  • Recommender Systems as tools for personalised decision support.
  • Interdisciplinary applications of decision making, decision support and personalisation: management, health and wellbeing, smart cities and urban planning, sustainability, government, leisure and tourism, networking and recruitment, etc.

The event particularly aims at exploring the existing – or potential – interaction between various AI techniques and applications, and state-of-the-art decision support and recommendation approaches. Young researchers (PhD students, postdocs) are particularly encouraged to attend for an opportunity to network with – and receive advice from – leading experts in the field.


Topics of Interest


Students, researchers and professionals with interest or expertise in the following topics are encouraged to attend:

  • Decision Support Systems: Theoretical Foundations, Models, Implementations and Applications
  • Recommender Systems: Theoretical Foundations, Models, Algorithms, Implementations, Evaluation and Applications
  • Decision-making, Decision Support and Personalisation based on…
    • Machine Learning
    • Data Analysis and Data Fusion
    • Expert and Information Systems, Knowledge-based Systems
    • Group and Multi-Criteria Decision Analysis
    • Context-Aware Recommendation
    • Reciprocal Recommender Systems
    • Artificial Intelligence Methods
    • Evolutionary Algorithms
    • Computer Vision
    • Natural Language Processing and Text Mining
    • Social Network Analysis
    • Financial Technologies
    • Robotics, Autonomous and Multi-Agent Systems
    • Fuzzy Systems and Uncertainty Modelling
    • Urban Analytics, Smart Cities and IoT
    • Fairness, Explainability, Privacy and Ethics
  • Applications: Security, Health & Wellbeing, Environment, Impact, Government, Smart Cities, Manufacturing, Tourism and Leisure…



Professor Francisco Herrera

Professor Francisco Herrera

University of Granada (Spain)

Title: Video Surveillance and Weapon Detection with Deep Learning

Professor Peter A Flach

Professor Peter Flach

University of Bristol; The Alan Turing Institute (United Kingdom)

Title: Will be available soon…

Professor Mounia Lalmas

Professor Mounia Lalmas

Spotify (United Kingdom)

Title: Personalising the Listening Experience

Dr Christoph Trattner

Dr Christoph Trattner

University of Bergen (Norway)

Title: Computational Analytics on the Web for Better Food Decision Making

Dr Julia Neidhardt

Dr Julia Neidhardt

TU Wien (Austria)

Title: Recommender Systems and Decision-making in the Tourism Domain .

Dr Matthijs Spaan

TU Delft (Netherlands)

Title: TBD.



Young researchers are invited to submit extended abstracts in English.

  • Any submissions within the scope of the Topics of Interest list are welcome.
  • We recommend a maximum of 4 pages in single column formatting, please use the conference Template for preparing your submission (Download Template).
  • The submission of the extended abstracts is intended for young researchers: PhD students, postdocs or academics/scientists who received their PhD on or after 2014.
  • Authors of accepted abstracts will be invited to showcase a poster – and optionally to present a demo – of their work, to network with and receive feedback from senior experts and attendees in general. Some selected authors will be also invited to deliver an 8-minute Flash Talk at a conference session.
  • The submission and acceptance of an extended abstract are not requirements for attendance. This conference is open for registration by any delegates from across the Turing Institute and abroad.

After evaluation, notification of acceptance will be provided to authors via email. Please note that the full submissions will not be published, but plans are underway to ellaborate a mini-proceedings booklet with short abstracts of accepted works.

Submission system now open! Submit your extended abstract here

Important dates

  • Submission of Extended Abstracts: 1st September 2019
  • Registration opens:September 2019
  • Notification of acceptance of Extended Abstracts: 14th September 2019
  • Registration closes: 31st October 2019
  • DSRS-Turing Conference: 21-22nd November 2019

Registration will be available through the event page in the Alan Turing Institute website

For enquiries, please contact us at:dsrs.turing@gmail.com

Looking forward to see you next November in London!

Ivan Palomares speaks and is interviewed at Search-Star’19 conference (Bath, UK)

The SearchStar 2019 conference on Analytics of Conversion was held in Bath last 27th June, organised by Search Star , an UK-based company founded in 2005 and focused on advancing digital strategies for marketing and advertisement.

Ivan Palomares from DSRS was one of the speakers at the event, where he delivered the talk Personalisation & Recommender Systems: Perspectives and Challenges.


A summary of the conference, including Ivan’s talk, was published in Search Star blog:

Iván offered his audience an insight into the revolutionary world of recommender systems and the role it will play in consumer choice in the future. Iván started by presenting the audience with a problem – ‘you want to buy a new book.’ Twenty years ago, we would have gone to a neighbourhood bookshop to ask for a recommendation. Nowadays, with the same question, we turn to the internet and are instantly overwhelmed by an extensive amount of results.
What’s the solution? Personalistion through recommender systems. These are evolutionary algorithms designed to provide tailored content to the user, by extracting knowledge about your preferences from previous choices and choices of similar users. If you’ve ever listened to songs on Spotify or bought from Amazon, then these recorded decisions are used to inform these systems about your preferences.
There are many techniques used to tailor these recommendations; you could either be served results based on your demographic, where you are at a certain time and the opening hours of the shops around you (context-aware), or choosing a selection of restaurants based on the reasoning that people with similar interests to you also enjoyed those restaurants (collaborative filtering).
There are challenges to this approach, specifically when they come across a ‘cold user,’ where they lack information on their preferences, or the essential requirement to combine multiple views and sources of user data.
We also had a significant question from the audience regarding data privacy, however Iván responded that all data used to make the recommendations are information that the user has provided either explicitly or implicitly, and it is on the usage of implicit user data where privacy and ethics concerns really come into scene.

Our team member was also interviewed prior to his talk. You can read the interview, entitled ‘What are Recommender Systems?’, here

Zijian Shi – Congratulations and Future Welcome to Bristol and DSRS!

Zijian Shi, our upcoming DSRS researcher

We are pleased to congratulate Mr Zijian Shi, who has obtained a prestigious UK-China PhD scholarship from the China Scholarship Council (CSC) to initiate his PhD studies at the University of Bristol, with the DSRS Lab, after summer 2019.

Zijian has  international experience studying abroad, having received two Masters’ Degrees: one on Management Sciences and Engineering from Tianjin University (China), one of our lab’s international collaborators and a top Chinese university; as well as one on Finance from The University of Texas at Dallas (United States).

Zijian has gained research expertise in decision support systems applied to financial analytics, and he has prior experience in scientific publications, e.g. an authored article resulting from Tianjin University’s collaborations with DSRS, published in the high-impact journal Knowledge-based Systems.

We look forward to welcome you to DSRS soon!

Professor Xueqing Wang (bottom center) and her lab at Tianjin University, with Zijian Shi sitting to the right.

DSRS and Bristol’s Jean Golding Institute organise RESULTS Interdisciplinary workshop – 23rd May 2019

RESULTS: REcommender Systems for engaging Users with healthy Living habiTS

The DSRS research group is organising an exciting interdisciplinary workshop, as part of the 2019 Data Week celebrated next May at Bristol’s Jean Golding Institute. See below for event information and our three confirmed speakers’ profile.

Registrationregister for this event via Eventbrite (PLACES LIMITED)

Event enquiries: Please contact Iván Palomares Carrascosa, workshop chair, for any event-related enquiries: i.palomares (at) bristol.ac.uk


This workshop is aimed at researchers, students, academics and practitioners across the Turing network of UK universities and beyond, as well as members of related industry sectors. 

There will be a discussion forum on the challenges and opportunities of personalisation approaches, data-driven Decision Support and Recommender Systems (RecSys) in the areas of fitness, wellbeing and promoting healthy living. We seek to bring together experts across the disciplines of Data Science, Decision Support and AI, Medical Sciences, Data Ethics, Psychology and Behavioural Sciences, Nutrition and Sports/Physical Activity.  

Event focus 

  1. Connecting experts across disciplines (health, nutrition and sport, etc.) with scientists in Data Science/AI and industry, to put forward potential solutions to unaddressed challenges involving personalisation and/or decision support 
  1. Data ethics implications and user data protection regulations in personalized healthcare and wellbeing.  

Participants with ongoing research activity on decision support approaches or RecSys for the health and wellbeing domains are encouraged to bring their laptops to showcase or discuss their results with experts in the field. 


Daniele Quercia, King’s College London & Nokia Bell Labs.

Healthy Cities: Tracking Population Health from Grocery Bags and Smart Watches 

Quercia,DanieleBio: Daniele Quercia is with the Department Head of Social Dynamics at Nokia Bell Labs Cambridge (UK) and Professor of Urban Informatics at the Center for Urban Science and Progress (CUSP) at King’s College London. He has been named one of Fortune magazine’s 2014 Data All-Stars, and spoke about “happy maps” at TED. His research has been focusing in the area of urban informatics and received best paper awards from Ubicomp 2014 and from ICWSM 2015, and an honourable mention from ICWSM 2013. He was Research Scientist at Yahoo Labs, a Horizon senior researcher at the University of Cambridge, and Postdoctoral Associate at the department of Urban Studies and Planning at MIT. He received his PhD from UC London. His thesis was sponsored by Microsoft Research and was nominated for BCS Best British PhD dissertation in Computer Science. 

Abstract: We will see how to aggregate both readings from consumer wearable devices and records of food purchases to track people’s well-being at scale. From 11,600 Nokia Health wearables, we collected readings of steps, sleep, and heart rate in the entire cities of London and San Francisco over the course of 1 year. Christmas and New Year’s eve were associated only with short-lived and minor disruptions, while both Brexit and Trump’s election greatly impacted people’s sleep and even heart rates. Then, for another entire year in London, we studied the association between food purchases in grocery stores, as measured by the digital traces of customer loyalty cards, and consumption of medicines. Our results show that analytics of digital records of grocery purchases can be used as a cheap and scalable tool for health surveillance: the distribution of the food nutrients is far more predictive of food-related illnesses (e.g. diabetes) than socio-economic conditions. 

Morgan Harvey, Northumbria University.

Balancing the Healthy with the Tasty: Recommending Nutritious Food that People Will Actually Want to Eat 

Morgan HarveyBio: Morgan Harvey is a Senior Lecturer of information science in the Department of Computer and Information Sciences at Northumbria University, Newcastle. He has been conducting research in the fields of information behaviour and retrieval and recommender systems for over a decade and has published over 50 peer-reviewed conference papers and journal articles, including in ACM SIGIR, ACM CIKM and ACM RecSys. Morgan received his PhD from the University of Strathclyde under the supervision of Prof. Ian Ruthven and has worked as a researcher at the University of Erlangen-Nuremberg in Germany and the University of Lugano in Switzerland. 

Abstract: Poor dietary habits are a major cause of today’s world health problems, especially in the developed world. Evidence shows that such issues can be prevented and sometimes even reversed through good nutrition, however, people are often very poor at judging the healthiness of their own diet and need support to implement positive changes. Computer and information technology, and particularly recommender systems, have been suggested as potential solutions to some of these problems but much of the necessary work is yet to be done. This talk will discuss existing research on the problem of recipe recommendation, the difficulty of finding a balance between healthy and enjoyable choices and early work investigating how people can be “nudged” into making healthier food choices. 

Max Western, University of Bath

Multidimensional physical activity: using the right data from wearable fitness trackers and providing the right feedback

Photo of Max WesternBio: Max is a lecturer in behavioural science within the Department for Health at the University of Bath. He has keen research interests in the factors that influence and facilitate health-enhancing physical activity behaviour, the application of digital health technologies for measurement and intervention and healthy ageing. In recent years Max has worked on several large lifestyle-based intervention trials funded by the MRC and NIHR. Specifically, he has helped develop and evaluate interdisciplinary projects aimed at reducing risk of future chronic disease using wearable monitoring technology to provide feedback on multidimensional physical activity; combatting physical frailty in older age through a community-based physical activity programme; and reducing the decline in cognitive function in older adults with mild-cognitive impairment using a digital web-based behaviour change intervention.

Abstract:  The benefits of regular physical activity behaviour on physical function and health are well documented and it is apparent that there are multiple dimensions to physical activity are important. The accurate measurement of physical activity is key to understanding the relationship between behaviour and health, providing credible feedback to participants and judging the success of behaviour change interventions aimed at increasing levels. Accelerometers are popularised as being the most objective and accurate format of assessment. This talk will draw on research data to discuss the strengths and limitations to their current use, such as their (in)appropriateness for comparisons with public health guidelines and the interpretation of what counts as physical activity, and propose solutions to these issues that should guide future applications of these measurement devices.

Event co-sponsored by: Jean Golding Institute for Data-Intensive Research, University of Bristol, and Bristol’s Intelligent Systems Lab