R&D Projects

Here is a list of projects where DSRS members are taking part – or have taken part – since the establishment of the group on 2018.


Projects listed in this page include international collaborations, partnership building initiatives, interdisciplinary research, etc.


Knowledge-Transfer Project: “The Engineering as a Facilitator for Sustainable Development Goals (SDGs). Artificial Intelligence and Emerging Digital Technologies”

Participating entities: DaSCI Research Institute (U. Granada), 12 participants; Spanish Royal Academy of Engineering (RAI); Ferrovial Spain.

Las empresas deben acelerar la integración de los ODS para lograr ...

Members – DaSCIFerrovial: infraestructuras sosteniblesLa Fundación Michelin España Portugal se une a 'Mujer e Ingeniería ...

Jean Golding Institute, University of Bristol – seed corn Project (PhD call): Evolutionary System for personalized wellbeing recommendations, A food and physical activity data-driven user study

Principal Investigator: Hugo Alcaraz (PhD student), Iván Palomares (Project supervisor)

Co-Investigators: John Cartlidge and Zoi Toumpakari (University of Bristol), Max Western (University of Bath).

Dates: April-September 2020.


Jean Golding Institute, University of Bristol – seed corn Project:  Automating food aggregation for nutrition and health research

Principal Investigator: Zoi Toumpakari

Co-Investigators: Iván Palomares andHugo Alcaraz-Herrera (DSRS), Daniele Quercia and Luca Maria Aiello (King’s College London & Nokia Bell Labs).

Dates: January-July 2020.


Alan Turing Fellowship: Promoting Healthy Living Habits via Recommender Systems

DSRS members and role: Ivan Palomares Carrascosa (Turing Fellow), Hugo Alcaraz-Herrera (project collaborators).

Project partners: Jean Golding Institute (University of  Bristol)

Start Date: 1st November 2018

Intelligent Decision-Making and Big Data Fusion Strategies in Recommender Systems: application to Point-of-Interest and Leisure Recommendation in Smartcities

DSRS members and role: Iván Palomares Carrascosa (Principal Investigator)

Type of project and funding body: International Strategic Fund (University of Bristol)

Multi-view Recommender System (Recsys) models exploit and combine multiple sources of heterogeneous data (user preferences, profile, contextual information, social trust, text reviews) across recommendation processes to provide end users with improved personalization services. This project aimed at kick-starting a collaboration between Sichuan University (China) and the University of Bristol. It investigates the role and potential benefits of multi-view Recsys approaches in nowadays data-pervaded domains, with particular application interest on personalization for tourism and leisure in smart cities.


Consensus-oriented group recommendation decision-making models and their applications

DSRS members and role: Iván Palomares Carrascosa (co-Investigator)

Type of project and funding body: Young researcher grant (National Science Foundation of China, NSFC). Principal Investigator: Dr. Hengjie Zhang

Group recommendation problems involving a group of users have become a hot topic in the field of Recsys. This project focuses on studying the consensus-based group recommendation problem to help groups of users finding consensual recommendation result(s), with potential decision support applications involving the use of designed Group Recsys approaches e.g. in tourism.


Expansion of Weighted Selective Aggregated Majority-OWA for Heterogeneous Group Decision with Uncertainty Analysis.

DSRS members and role: Binyamin bin Yusoff (Principal Investigator), Iván Palomares Carrascosa (co-Investigator)

Type of project and funding body: Industrial Attachment Programme, University of Malaysia Terengannu.

Weighted selective aggregated majority-ordered weighted average (WSAM-OWA) is a type of additive aggregation process that extends the majority additive-OWA (MA-OWA). Its main feature is it can be used to model the concept of majority opinion (heterogeneous case) with respect to cardinality of the argument values. The WSAM-OWA is still in its early stage of development and therefore has a lot of room for expansion. By generalizing the WSAM-OWA, a wide range of aggregation operators can be derived and some sets of mathematical properties can be potentially examined. Even though the MA-OWA and WSAM-OWA has been successfully applied in group decision models, it still has some limitations especially in representing the imprecise or uncertain data such as subjective judgments of experts. This project therefore aims at expanding the concept of WSAM-OWA to cope with group decision making under uncertainty.