شماره ركورد كنفرانس :
3297
عنوان مقاله :
Inferring User Preference Using Distance Dependent Chinese Restaurant Process and Weighted Distribution for a Content Based Recommender
عنوان به زبان ديگر :
Inferring User Preference Using Distance Dependent Chinese Restaurant Process and Weighted Distribution for a Content Based Recommender
پديدآورندگان :
Rahimpour Cami Bagher Faculty of Computer Engineering and Information Technology Shahrood University of Technology Shahrood - Iran , Hassanpour Hamid Faculty of Computer Engineering and Information Technology Shahrood University of Technology Shahrood - Iran , Mashayekhi Hoda Faculty of Computer Engineering and Information Technology Shahrood University of Technology Shahrood - Iran
كليدواژه :
predicting user preference , extracting user interests , dynamic user modeling , Content-based recommender systems
عنوان كنفرانس :
نوزدهمين سمپوزيوم بين المللي هوش مصنوعي و پردازش سيگنال
چكيده لاتين :
Nowadays websites provide a vast number of resources for users. Recommender systems have been developed as an essential element of these websites to provide a personalized environment for users. They help users to retrieve interested resources from large sets of available resources. Due to the dynamic feature of user preference, constructing an appropriate model to estimate the user preference is the major task of recommender systems. Profile matching and latent factors are two main approaches to identify user preference. In this paper, we employed the latent factor and profile matching to cluster the user profile and identify user preference, respectively. The proposed method uses the Distance Dependent Chines Restaurant Process as a Bayesian nonparametric framework to extract the latent factors from the user profile. These latent factors are mapped to user interests and a weighted distribution is used to identify user preferences. We evaluate the proposed method using a real-world data-set that contains news tweets of a news agency (BBC). The experimental results and comparisons show the superior recommendation accuracy of the proposed approach related to existing methods, and its ability to effectively evolve over time.
