DocumentCode :
553181
Title :
Modeling user and item biases with Gaussian distribution for collaborative filtering
Author :
Mingkui Liu ; Xiaohong Jiang
Author_Institution :
Coll. of Comput. Sci., Zhejiang Univ., Hangzhou, China
Volume :
3
fYear :
2011
fDate :
26-28 July 2011
Firstpage :
2070
Lastpage :
2073
Abstract :
The collaborative filtering approach to recommender systems focuses on learning predictive models of user preferences, interests and behavior from community data, that is, the behavior of other available users. Matrix Factorization (MF) based approaches have been proven to be efficient collaborative filtering algorithm for rating-based recommender systems. But existing MF algorithms have several disadvantages, including ignoring the distribution of the ubiquitous user and item biases. In this work we present an improved probabilistic matrix factorization (IPMF) algorithm and its graphical model. We analyzed the statistical pattern of user and item biases in the MovieLens dataset. The user and item biases are normally distributed. The improved model takes user and item preference biases into account, thereby building a more accurate model. Further accuracy improvements are achieved by extending this model with nonnegative user feature vectors. We evaluated these methods on the MovieLens dataset, and we show that our experimental results are better than those previously reported on this dataset.
Keywords :
Gaussian distribution; information filtering; matrix decomposition; normal distribution; recommender systems; statistical analysis; user modelling; Gaussian distribution; IPMF algorithm; MovieLens dataset; collaborative filtering; graphical model; improved probabilistic matrix factorization; item biases; learning predictive models; normal distribution; rating based recommender systems; statistical pattern analysis; user interest; Accuracy; Collaboration; Graphical models; Motion pictures; Prediction algorithms; Probabilistic logic; Recommender systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems and Knowledge Discovery (FSKD), 2011 Eighth International Conference on
Conference_Location :
Shanghai
Print_ISBN :
978-1-61284-180-9
Type :
conf
DOI :
10.1109/FSKD.2011.6019834
Filename :
6019834
Link To Document :
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