Miquel Montaner

Collaborative Recommender Agents

State-of-the-art and new techniques

• Broschiertes Buch

Produktbeschreibung

This thesis focusses on the study of AI techniques which improve the performance of recommender systems. Initially, a detailed analysis of the current state- of-the-art in this field has been carried out. This work has been organised as a taxonomy where existing recommender systems on the Internet are classified. Secondly, this thesis proposes a new CBR approach to recommendation. CBR is suitable for recommender systems due to its being based on experience and human reasoning. A forgetting mechanism is also proposed for case-based profiles that controls the relevance and age of past experiences. Thirdly, this thesis proposes the agentification of recommender systems in order to take advantage of interesting agent properties such as proactivity, encapsulation or social ability. Collaboration among agents is performed with the opinion-based filtering method and the collaborative filtering method through trust. Both are based on a social model of trust making agents less vulnerable to others while collaborating. Finally, this thesis also proposes an evaluation procedure for recommender systems that allows a scientific discussion of the results.

Produktdetails

• Verlag: LAP Lambert Academic Publishing
• Seitenzahl: 240
• Erscheinungstermin: 16. Februar 2011
• Englisch
• Abmessung: 220mm x 150mm x 15mm
• Gewicht: 375g
• ISBN-13: 9783838354262
• ISBN-10: 3838354265
• Artikelnr.: 33072176

Autorenporträt

PhD degree in Computer Engineering and the MBA from the University of Girona (UdG) with the research focus on Artificial Intelligence. He manages R&D at EASY, UdG Technology Center, and one of its spin-offs, Strategic Attention Management. He is granted a research fellowship from the Catalan Institute for Research and Advanced Studies.