@article {820,
	title = {Rating Prediction Quality Enhancement in Low-Density Collaborative Filtering Datasets},
	journal = {Big Data and Cognitive Computing},
	volume = {7},
	year = {2023},
	month = {mar},
	pages = {59},
	abstract = {<p>Collaborative filtering has proved to be one of the most popular and successful rating prediction techniques over the last few years. In collaborative filtering, each rating prediction, concerning a product or a service, is based on the rating values that users that are considered {\textquotedblleft}close{\textquotedblright} to the user for whom the prediction is being generated have given to the same product or service. In general, {\textquotedblleft}close{\textquotedblright} users for some user u correspond to users that have rated items similarly to u and these users are termed as {\textquotedblleft}near neighbors{\textquotedblright}. As a result, the more reliable these near neighbors are, the more successful predictions the collaborative filtering system will compute and ultimately, the more successful recommendations the recommender system will generate. However, when the dataset{\textquoteright}s density is relatively low, it is hard to find reliable near neighbors and hence many predictions fail, resulting in low recommender system reliability. In this work, we present a method that enhances rating prediction quality in low-density collaborative filtering datasets, by considering predictions whose features are associated with high prediction accuracy as additional ratings. The presented method{\textquoteright}s efficacy and applicability are substantiated through an extensive multi-parameter evaluation process, using widely acceptable low-density collaborative filtering datasets.</p>
},
	keywords = {collaborative filtering, Evaluation, low density datasets, personalization, rating prediction quality, Recommender Systems, reliability},
	doi = {10.3390/bdcc7020059},
	url = {https://www.mdpi.com/2504-2289/7/2/59},
	author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos and Stefanos Ougiaroglou}
}
@book {DBLP:reference/db/2018,
	title = {Encyclopedia of Database Systems, Second Edition},
	year = {2018},
	publisher = {Springer},
	organization = {Springer},
	isbn = {978-1-4614-8266-6},
	doi = {10.1007/978-1-4614-8265-9},
	url = {https://doi.org/10.1007/978-1-4614-8265-9},
	editor = {Ling Liu and M. Tamer {\"O}zsu}
}
@book {DBLP:reference/db/2009,
	title = {Encyclopedia of Database Systems},
	year = {2009},
	publisher = {Springer US},
	organization = {Springer US},
	isbn = {978-0-387-35544-3},
	doi = {10.1007/978-0-387-39940-9},
	url = {https://doi.org/10.1007/978-0-387-39940-9},
	editor = {Ling Liu and M. Tamer {\"O}zsu}
}
@proceedings {DBLP:conf/icde/2007,
	title = {Proceedings of the 23rd International Conference on Data Engineering, ICDE 2007, The Marmara Hotel, Istanbul, Turkey, April 15-20, 2007},
	year = {2007},
	publisher = {IEEE Computer Society},
	isbn = {1-4244-0802-4},
	url = {http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=4221634},
	editor = {Rada Chirkova and Asuman Dogac and M. Tamer {\"O}zsu and Timos K. Sellis}
}
@proceedings {DBLP:conf/caise/2002,
	title = {Advanced Information Systems Engineering, 14th International Conference, CAiSE 2002, Toronto, Canada, May 27-31, 2002, Proceedings},
	volume = {2348},
	year = {2002},
	publisher = {Springer},
	isbn = {3-540-43738-X},
	doi = {10.1007/3-540-47961-9},
	url = {https://doi.org/10.1007/3-540-47961-9},
	editor = {Anne Banks Pidduck and John Mylopoulos and Carson C. Woo and M. Tamer {\"O}zsu}
}
