@article {margaris-IJBDI-2019-b,
	title = {Improving collaborative filtering{\textquoteright}s rating prediction accuracy by considering users{\textquoteright} dynamic rating variability},
	journal = {International Journal of Big Data Intelligence},
	volume = {7},
	year = {2020},
	chapter = {59--71},
	abstract = {<p>Users that populate ratings databases, follow different marking practices, in the sense that some are stricter, while others are more lenient. Similarly, users{\textquoteright} rating practices may also differ in rating variability, in the sense that some users may be entering ratings close to their mean, while other users may be entering more extreme ratings, close to the limits of the rating scale. While this aspect has been recently addressed through the computation and exploitation of an overall rating variability measure per user, the fact that user rating practices may vary along the user{\textquoteright}s rating history time axis may render the use of the overall rating variability measure inappropriate for performing the rating prediction adjustment. In this work, we (1) propose an algorithm that considers two variability metrics per user, the global (overall) and the local one, with the latter representing the user{\textquoteright}s variability at prediction time, (2) present alternative methods for computing a user{\textquoteright}s local variability and (3) evaluate the performance of the proposed algorithm in terms of rating prediction quality and compare it against the state-of-the-art algorithm that employs a single variability metric in the rating prediction computation process.</p>
},
	keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, prediction accuracy, users{\textquoteright} ratings dynamic variability},
	doi = {10.1504/ijbdi.2020.107373},
	author = {Dionisis Margaris and Costas Vassilakis}
}
@article {margaris-IJBDI-2019-a,
	title = {Improving collaborative filtering{\textquoteright}s rating prediction coverage in sparse datasets by exploiting the {\textquotedblleft}friend of a friend{\textquotedblright} concept},
	journal = {International Journal of Big Data Intelligence},
	volume = {7},
	year = {2020},
	chapter = {47},
	abstract = {<p>Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, by examining the likeness of already entered ratings. Users with highly similar tastes are termed {\textquotedblleft}near neighbours{\textquotedblright} and recommendations for a user are based on her near neighbours{\textquoteright} ratings. However, for a number of users no near neighbours can be found, a problem termed as the {\textquotedblleft}gray sheep{\textquotedblright} problem. This problem is more intense in sparse datasets, i.e. datasets with relatively small number of ratings, compared to the number of users and items. In this work, we propose an algorithm for alleviating this problem by exploiting the friend of a friend (FOAF) concept. The proposed algorithm, CFfoaf, has been evaluated against eight widely used sparse datasets and under two widely used collaborative filtering correlation metrics, namely the Pearson Correlation Coefficient and the Cosine Similarity and has been proven to be particularly effective in increasing the percentage of users for which personalized recommendations can be formulated in the context of sparse datasets, while at the same time improving rating prediction quality.</p>
},
	keywords = {collaborative filtering, Cosine Similarity, Evaluation, friend-of-a-friend, Pearson Correlation Coefficient, prediction accuracy, Recommender Systems, Sparse Datasets},
	issn = {2053-1389},
	doi = {10.1504/ijbdi.2020.106178},
	author = {Dionisis Margaris and Costas Vassilakis}
}
