Using mixture models for collaborative filtering
نویسندگان
چکیده
منابع مشابه
Relevance Models for Collaborative Filtering Relevance Models for Collaborative Filtering
The Master said, " When I walk along with two others, they may serve me as my teachers. I will select their good qualities and follow them, their bad qualities and avoid them. " The Lunyu: BooK VII Shu'er Confucius, 551 BCE-479 BCE to my family for making it possible Summary Collaborative filtering is the common technique of predicting the interests of a user by collecting preference informatio...
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Recent projects in collaborative filtering and information filtering address the task of inferring user preference relationships for products or information. The data on which these inferences are based typically consists of pairs of people and items. The items may be information sources (such as web pages or newspaper articles) or products (such as books, software, movies or CDs). We are inter...
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Low-rank matrix approximation (LRMA) methods have achieved excellent accuracy among today’s collaborative filtering (CF) methods. In existing LRMA methods, the rank of user/item feature matrices is typically fixed, i.e., the same rank is adopted to describe all users/items. However, our studies show that submatrices with different ranks could coexist in the same user-item rating matrix, so that...
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Gaussian Mixture Models (GMMs) of power spectral densities of speech and noise are used with explicit Bayesian estimations in Wiener filtering of noisy speech. No assumption is made on the nature or stationarity of the noise. No voice activity detection (VAD) or any other means is employed to estimate the input SNR. The GMM mean vectors are used to form sets of over-determined system of equatio...
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ژورنال
عنوان ژورنال: Journal of Computer and System Sciences
سال: 2008
ISSN: 0022-0000
DOI: 10.1016/j.jcss.2007.04.013