@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}
}
@article {813,
	title = {On Producing Accurate Rating Predictions in Sparse Collaborative Filtering Datasets},
	journal = {Information},
	volume = {13},
	year = {2022},
	month = {jun},
	pages = {302},
	abstract = {<p>The typical goal of a collaborative filtering algorithm is the minimisation of the deviation between rating predictions and factual user ratings so that the recommender system offers suggestions for appropriate items, achieving a higher prediction value. The datasets on which collaborative filtering algorithms are applied vary in terms of sparsity, i.e., regarding the percentage of empty cells in the user{\textendash}item rating matrices. Sparsity is an important factor affecting rating prediction accuracy, since research has proven that collaborative filtering over sparse datasets exhibits a lower accuracy. The present work aims to explore, in a broader context, the factors related to rating prediction accuracy in sparse collaborative filtering datasets, indicating that recommending the items that simply achieve higher prediction values than others, without considering other factors, in some cases, can reduce recommendation accuracy and negatively affect the recommender system{\textquoteright}s success. An extensive evaluation is conducted using sparse collaborative filtering datasets. It is found that the number of near neighbours used for the prediction formulation, the rating average of the user for whom the prediction is generated and the rating average of the item concerning the prediction can indicate, in many cases, whether the rating prediction produced is reliable or not.</p>
},
	keywords = {accuracy, collaborative filtering, Evaluation, Personalisation, Rating prediction, Recommender Systems, reliability, Sparse Datasets},
	doi = {https://doi.org/10.3390/info13060302},
	author = {Dionisis Margaris and Costas Vassilakis and Dimitris Spiliotopoulos}
}
