Recommender Systems Handbook

This second edition of a well-received text, with 20 new chapters, presents a coherent and unified repository of recommender systems’ major concepts, theories, methodologies, trends, and challenges. A variety of real-world applications and detailed case studies are included. In addition to wholesale...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Άλλοι συγγραφείς: Ricci, Francesco (Επιμελητής έκδοσης), Rokach, Lior (Επιμελητής έκδοσης), Shapira, Bracha (Επιμελητής έκδοσης)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Boston, MA : Springer US : Imprint: Springer, 2015.
Έκδοση:2nd ed. 2015.
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
Πίνακας περιεχομένων:
  • Recommender Systems: Introduction and Challenges
  • A Comprehensive Survey of Neighborhood-based Recommendation Methods
  • Advances in Collaborative Filtering
  • Semantics-aware Content-based Recommender Systems
  • Constraint-based Recommender Systems
  • Context-Aware Recommender Systems
  • Data Mining Methods for Recommender Systems
  • Evaluating Recommender Systems
  • Evaluating Recommender Systems with User Experiments
  • Explaining Recommendations: Design and Evaluation
  • Recommender Systems in Industry: A Netflix Case Study
  • Panorama of Recommender Systems to Support Learning
  • Music Recommender Systems
  • The Anatomy of Mobile Location-Based Recommender Systems
  • Social Recommender Systems
  • People-to-People Reciprocal Recommenders
  • Collaboration, Reputation and Recommender Systems in Social Web Search
  • Human Decision Making and Recommender Systems
  • Privacy Aspects of Recommender Systems
  • Source Factors in Recommender System Credibility Evaluation
  • Personality and Recommender Systems
  • Group Recommender Systems: Aggregation, Satisfaction and Group Attributes
  • Aggregation Functions for Recommender Systems
  • Active Learning in Recommender Systems
  • Multi-Criteria Recommender Systems
  • Novelty and Diversity in Recommender Systems
  • Cross-domain Recommender Systems
  • Robust Collaborative Recommendation.