@article{cami_user_2018,
	title = {User preferences modeling using dirichlet process mixture model for a content-based recommender system},
	issn = {0950-7051},
	url = {http://www.sciencedirect.com/science/article/pii/S0950705118304799},
	doi = {10.1016/j.knosys.2018.09.028},
	abstract = {Recommender systems have been developed to assist users in retrieving
relevant resources. Collaborative and content-based filtering are two
basic approaches that are used in recommender systems. The former employs
the feedback of users with similar interests, while the latter is based on
the feature of the selected resources by each user. Recommender systems
can consider users’ behavior to more accurately estimate their preferences
via a list of recommendations. However, the existing approaches rarely
consider both interests and preferences of the users. Also, the dynamic
nature of user behavior poses an additional challenge for recommender
systems. In this paper, we consider the interactions of each individual
user, and analyze them to propose a user model and capture user’s
interests. We construct the user model based on a Bayesian nonparametric
framework, called the Dirichlet Process Mixture Model. The proposed model
evolves following the dynamic nature of user behavior to adapt both the
user interests and preferences. We implemented the proposed model and
evaluated it using both the MovieLens dataset, and a real-world dataset
that contains news tweets from five news channels (New York Times, BBC,
CNN, Reuters and Associated Press). The experimental results and
comparisons with several recently developed approaches show the
superiority in accuracy of the proposed approach, and its ability to adapt
with user behavior over time.},
	journal = {Knowledge-Based Systems},
	author = {Cami, Bagher Rahimpour and Hassanpour, Hamid and Mashayekhi, Hoda},
	month = sep,
	year = {2018},
	keywords = {Temporal content-based recommender systems, User behavior modeling, User preferences modeling},
}

{"_id":"SWmZo6GAsikYmHLeH","bibbaseid":"cami-hassanpour-mashayekhi-userpreferencesmodelingusingdirichletprocessmixturemodelforacontentbasedrecommendersystem-2018","authorIDs":[],"author_short":["Cami, B. R.","Hassanpour, H.","Mashayekhi, H."],"bibdata":{"bibtype":"article","type":"article","title":"User preferences modeling using dirichlet process mixture model for a content-based recommender system","issn":"0950-7051","url":"http://www.sciencedirect.com/science/article/pii/S0950705118304799","doi":"10.1016/j.knosys.2018.09.028","abstract":"Recommender systems have been developed to assist users in retrieving relevant resources. Collaborative and content-based filtering are two basic approaches that are used in recommender systems. The former employs the feedback of users with similar interests, while the latter is based on the feature of the selected resources by each user. Recommender systems can consider users’ behavior to more accurately estimate their preferences via a list of recommendations. However, the existing approaches rarely consider both interests and preferences of the users. Also, the dynamic nature of user behavior poses an additional challenge for recommender systems. In this paper, we consider the interactions of each individual user, and analyze them to propose a user model and capture user’s interests. We construct the user model based on a Bayesian nonparametric framework, called the Dirichlet Process Mixture Model. The proposed model evolves following the dynamic nature of user behavior to adapt both the user interests and preferences. We implemented the proposed model and evaluated it using both the MovieLens dataset, and a real-world dataset that contains news tweets from five news channels (New York Times, BBC, CNN, Reuters and Associated Press). The experimental results and comparisons with several recently developed approaches show the superiority in accuracy of the proposed approach, and its ability to adapt with user behavior over time.","journal":"Knowledge-Based Systems","author":[{"propositions":[],"lastnames":["Cami"],"firstnames":["Bagher","Rahimpour"],"suffixes":[]},{"propositions":[],"lastnames":["Hassanpour"],"firstnames":["Hamid"],"suffixes":[]},{"propositions":[],"lastnames":["Mashayekhi"],"firstnames":["Hoda"],"suffixes":[]}],"month":"September","year":"2018","keywords":"Temporal content-based recommender systems, User behavior modeling, User preferences modeling","bibtex":"@article{cami_user_2018,\n\ttitle = {User preferences modeling using dirichlet process mixture model for a content-based recommender system},\n\tissn = {0950-7051},\n\turl = {http://www.sciencedirect.com/science/article/pii/S0950705118304799},\n\tdoi = {10.1016/j.knosys.2018.09.028},\n\tabstract = {Recommender systems have been developed to assist users in retrieving\nrelevant resources. Collaborative and content-based filtering are two\nbasic approaches that are used in recommender systems. The former employs\nthe feedback of users with similar interests, while the latter is based on\nthe feature of the selected resources by each user. Recommender systems\ncan consider users’ behavior to more accurately estimate their preferences\nvia a list of recommendations. However, the existing approaches rarely\nconsider both interests and preferences of the users. Also, the dynamic\nnature of user behavior poses an additional challenge for recommender\nsystems. In this paper, we consider the interactions of each individual\nuser, and analyze them to propose a user model and capture user’s\ninterests. We construct the user model based on a Bayesian nonparametric\nframework, called the Dirichlet Process Mixture Model. The proposed model\nevolves following the dynamic nature of user behavior to adapt both the\nuser interests and preferences. We implemented the proposed model and\nevaluated it using both the MovieLens dataset, and a real-world dataset\nthat contains news tweets from five news channels (New York Times, BBC,\nCNN, Reuters and Associated Press). The experimental results and\ncomparisons with several recently developed approaches show the\nsuperiority in accuracy of the proposed approach, and its ability to adapt\nwith user behavior over time.},\n\tjournal = {Knowledge-Based Systems},\n\tauthor = {Cami, Bagher Rahimpour and Hassanpour, Hamid and Mashayekhi, Hoda},\n\tmonth = sep,\n\tyear = {2018},\n\tkeywords = {Temporal content-based recommender systems, User behavior modeling, User preferences modeling},\n}\n\n","author_short":["Cami, B. R.","Hassanpour, H.","Mashayekhi, H."],"key":"cami_user_2018","id":"cami_user_2018","bibbaseid":"cami-hassanpour-mashayekhi-userpreferencesmodelingusingdirichletprocessmixturemodelforacontentbasedrecommendersystem-2018","role":"author","urls":{"Paper":"http://www.sciencedirect.com/science/article/pii/S0950705118304799"},"keyword":["Temporal content-based recommender systems","User behavior modeling","User preferences modeling"],"metadata":{"authorlinks":{}},"downloads":1},"bibtype":"article","biburl":"https://api.zotero.org/users/6655/collections/TJPPJ92X/items?key=VFvZhZXIoHNBbzoLZ1IM2zgf&format=bibtex&limit=100","creationDate":"2020-03-27T02:34:35.404Z","downloads":1,"keywords":["temporal content-based recommender systems","user behavior modeling","user preferences modeling"],"search_terms":["user","preferences","modeling","using","dirichlet","process","mixture","model","content","based","recommender","system","cami","hassanpour","mashayekhi"],"title":"User preferences modeling using dirichlet process mixture model for a content-based recommender system","year":2018,"dataSources":["5Dp4QphkvpvNA33zi","jfoasiDDpStqkkoZB","BiuuFc45aHCgJqDLY"]}