Available recommender systems mostly provide recommendations based on the users’ preferences by utilizing traditional methods such as collaborative filtering which only relies on the similarities between users and items. However, collaborative filtering might lead to provide poor recommendation because it does not rely on other useful available data such as users’ locations and hence the accuracy of the recommendations could be very low and inefficient.
%0 Journal Article
%1 conf/ifip13/ThengTT10a
%A Madadipouya, Kasra
%D 2015
%J International Journal on Foundations of Computer Science & Technology (IJFCST)
%K Movie Recommender recommender system
%N 4
%P 7
%R 10.5121/ijfcst.2015.5402
%T A LOCATION-BASED MOVIE RECOMMENDER SYSTEM USING COLLABORATIVE FILTERING
%U https://wireilla.com/papers/ijfcst/V5N4/5415ijfcst02.pdf
%V 5
%X Available recommender systems mostly provide recommendations based on the users’ preferences by utilizing traditional methods such as collaborative filtering which only relies on the similarities between users and items. However, collaborative filtering might lead to provide poor recommendation because it does not rely on other useful available data such as users’ locations and hence the accuracy of the recommendations could be very low and inefficient.
@article{conf/ifip13/ThengTT10a,
abstract = {Available recommender systems mostly provide recommendations based on the users’ preferences by utilizing traditional methods such as collaborative filtering which only relies on the similarities between users and items. However, collaborative filtering might lead to provide poor recommendation because it does not rely on other useful available data such as users’ locations and hence the accuracy of the recommendations could be very low and inefficient.},
added-at = {2018-08-10T08:03:49.000+0200},
author = {Madadipouya, Kasra},
biburl = {https://www.bibsonomy.org/bibtex/268f480881f1fc4a3d0accb85283e4704/devino},
doi = {10.5121/ijfcst.2015.5402},
ee = {https://doi.org/10.1007/978-3-642-15231-3_32},
interhash = {b1d074b3099c09fea829a9f9bf2762bd},
intrahash = {68f480881f1fc4a3d0accb85283e4704},
journal = {International Journal on Foundations of Computer Science & Technology (IJFCST) },
keywords = {Movie Recommender recommender system},
language = {english},
month = {july},
number = 4,
pages = 7,
timestamp = {2018-08-10T08:03:49.000+0200},
title = {A LOCATION-BASED MOVIE RECOMMENDER SYSTEM USING COLLABORATIVE FILTERING
},
url = {https://wireilla.com/papers/ijfcst/V5N4/5415ijfcst02.pdf},
volume = 5,
year = 2015
}