Title :
Comparing Preference Models in Recommender Systems
Author :
Juntao Liu;Dewei Deng;Hanbao Wu;Caihua Wu
Author_Institution :
709th Res. Inst., China Shipbuilding Ind. Corp., Wuhan, China
Abstract :
How to represent the users´ preference is one of the principle problems in widely used recommender systems. To address this problem, in this paper, the expressibility, space complexity and learning complexity of different kinds of preference models are investigated. The recommendation performances of these models are also compared on a real-life dataset. Considering both the expressibility and computational complexity, the quadric model is the most suited to recommender systems.
Keywords :
"Computational modeling","Complexity theory","Regression tree analysis","Recommender systems","Numerical models","Probabilistic logic","Testing"
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2015 7th International Conference on
Print_ISBN :
978-1-4799-8645-3
DOI :
10.1109/IHMSC.2015.61
