• Publications
  • Influence
AVATAR: an improved solution for personalized TV based on semantic inference
The generalized arrival of the digital TV will bring a significant increase in the amount of channels and programs available to end users, with many more difficulties for them to find interestingExpand
  • 123
  • 6
  • PDF
Exploiting synergies between semantic reasoning and personalization strategies in intelligent recommender systems: A case study
Current recommender systems attempt to identify appealing items for a user by applying syntactic matching techniques, which suffer from significant limitations that reduce the quality of the offeredExpand
  • 57
  • 6
Providing entertainment by content-based filtering and semantic reasoning in intelligent recommender systems
Recommender systems arose in view of the information overload present in numerous domains. The so-called content-based recommenders offer products similar to those the users liked in the past.Expand
  • 89
  • 5
REENACT: A step forward in immersive learning about Human History by augmented reality, role playing and social networking
Abstract Classical pedagogy about Human History has dealt with many historic events as a mere collection of dates, locations and a number of confronted sides with a final result of victory or defeat.Expand
  • 23
  • 4
What's on tv tonight? An efficient and effective personalized recommender system of TV programs
This paper introduces Queveo.tv: a personalized TV program recommendation system. The proposed hybrid approach (combining content-filtering techniques with those based on collaborative filtering)Expand
  • 47
  • 3
A flexible semantic inference methodology to reason about user preferences in knowledge-based recommender systems
Recommender systems arose with the goal of helping users search in overloaded information domains (like e-commerce, e-learning or Digital TV). These tools automatically select items (commercialExpand
  • 123
  • 2
An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles
Recommender systems in online shopping automatically select the most appropriate items to each user, thus shortening his/her product searching time in the shops and adapting the selection as his/herExpand
  • 45
  • 2
Exploring synergies between content-based filtering and Spreading Activation techniques in knowledge-based recommender systems
Abstract Recommender systems fight information overload by selecting automatically items that match the personal preferences of each user. The so-called content-based recommenders suggest itemsExpand
  • 52
  • 2
  • PDF
TV program recommendation for groups based on muldimensional TV-anytime classifications
The advent of Digital TV and Personal Digital Recorders promise to change the way people watch TV. The higher efficiency of digital coding will lead to increasing the number of contents offered toExpand
  • 47
  • 2
Incentivized provision of metadata, semantic reasoning and time-driven filtering: Making a puzzle of personalized e-commerce
e-Commerce recommender systems select potentially interesting products for users by looking at their purchase histories and preferences. In order to compare the available products against thoseExpand
  • 13
  • 2