Title :
Multicriteria collaborative filtering by Bayesian model-based user profiling
Author :
Samatthiyadikun, Pannawit ; Takasu, Atsuhiro ; Maneeroj, Saranya
Author_Institution :
Chulalongkorn Univ., Bangkok, Thailand
Abstract :
This paper proposes a Bayesian model for multicriteria (MC) recommender systems, which are useful tools for delivering information to those who require it. Such systems usually handle a single overall rating score to capture user´s preferences. Recently proposed MC recommender systems use multiple scores evaluated from various aspects to obtain a more elaborate user profile. Our proposed model maps users and items to their groups via corresponding latent topics. We empirically evaluated the proposed model and showed that: (1) the Bayesian model is effective in estimating many parameters required for MC recommendation models; and (2) the multinomial distribution, which is usually used in latent models, is insufficient for predicting absolute rating scores.
Keywords :
binomial distribution; collaborative filtering; data handling; parameter estimation; recommender systems; user modelling; Bayesian model-based user profiling; MC recommender systems; absolute rating score prediction; information delivery; multicriteria collaborative filtering; multinomial distribution; parameter estimation; single overall rating score handling; user preferences; user profile; Bayesian methods; Predictive models; Probabilistic logic; Probability distribution; Recommender systems; TV; Vectors;
Conference_Titel :
Information Reuse and Integration (IRI), 2012 IEEE 13th International Conference on
Conference_Location :
Las Vegas, NV
Print_ISBN :
978-1-4673-2282-9
Electronic_ISBN :
978-1-4673-2283-6
DOI :
10.1109/IRI.2012.6303000