Computational intelligent methods for trusting in social networks

  1. NUÑEZ GONZALEZ, JOSE DAVID
Zuzendaria:
  1. Manuel Graña Romay Zuzendaria

Defentsa unibertsitatea: Universidad del País Vasco - Euskal Herriko Unibertsitatea

Fecha de defensa: 2016(e)ko otsaila-(a)k 26

Epaimahaia:
  1. Michal Wozniak Presidentea
  2. Borja Fernández Gauna Idazkaria
  3. Alexandre Manhaes Savio Kidea
  4. Carlos Andrés Toro Rodríguez Kidea
  5. José Miguel Alonso Kidea
Saila:
  1. Konputazio Zientzia eta Adimen Artifiziala

Mota: Tesia

Teseo: 449395 DIALNET lock_openADDI editor

Laburpena

This Thesis covers three research lines of Social Networks. The first proposed reseach line is related with Trust. Different ways of feature extraction are proposed for Trust Prediction comparing results with classic methods. The problem of bad balanced datasets is covered in this work. The second proposed reseach line is related with Recommendation Systems. Two experiments are proposed in this work. The first experiment is about recipe generation with a bread machine. The second experiment is about product generation based on rating given by users. The third research line is related with Influence Maximization. In this work a new heuristic method is proposed to give the minimal set of nodes that maximizes the influence of the network.