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

  • Palomares Carrascosa, Ivan (Principal Investigator)
  • Dong, Yucheng (Collaborator)

Project Details


Recommender Systems (RS) provide users with tailored information to meet their needs in contexts of information overload. They help “matching the right user with the right products/services at the right time”. Multi-view RS 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 aims at taking a novel and revolutionary step forward in the state-of-the-art of RS modeling, and its application in nowadays data-pervaded domains. It will lay the foundations for studying intelligent data fusion mechanisms and adaptive aggregation strategies, with opinion dynamics to enhance consensual recommendations for groups of users. Follow-up applications will focus on individual/group personalization for tourism and leisure in smart cities.
Effective start/end date26/03/1811/04/18


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