@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}
}
@conference {margarisIISA_CIRPaC,
	title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Accuracy by Introducing the Common Item Rating Past Criterion},
	booktitle = {Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019)},
	year = {2019},
	month = {jul},
	abstract = {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 neighbors{\textquotedblright} and recommendations for a user are based on his near neighbors{\textquoteright} ratings. In order to measure similarity between users, so as to determine a user{\textquoteright}s NNs, a similarity metric is used. Insofar, similarity metrics proposed in the literature either consider all user ratings equally or take into account temporal variations within the users{\textquoteright} or items{\textquoteright} ratings history. However users{\textquoteright} ratings are co-shaped according to the experiences that they had in the past; therefore if two users enter similar (or dissimilar) ratings for an item while having experienced {\textendash}to a large extent the same items in the past, this constitutes stronger evidence about user similarity (or dissimilarity). Insofar however, no similarity metric takes into account this aspect. In this work, we (1) propose an algorithm that considers the common item rating past in the rating prediction computation process, aiming to improve rating prediction quality, and (2) evaluate the proposed algorithm against seven widely used datasets, both dense and sparse, and considering two widely used user similarity metrics.},
	keywords = {collaborative filtering, Common Item Rating Past Criterion, Cosine Similarity, Evaluation, Items{\textquoteright} Rating Sequence, Pearson Correlation Coeffi-cient},
	author = {Dionisis Margaris and Dionysios Vasilopoulos and Costas Vassilakis and Dimitris Spiliotopoulos}
}
@conference {margaris_iisa2019_VNNs,
	title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Coverage in Sparse Datasets through the Introduction of Virtual Near Neighbors},
	booktitle = {Proceedings of the 10th International Conference on Information, Intelligence, Systems and Applications (IISA2019)},
	year = {2019},
	month = {jul},
	abstract = {Collaborative filtering creates personalized recommendations by considering ratings entered by users. Collaborative filtering algorithms initially detect users whose likings are alike, by ex-ploring the similarity between ratings that have insofar been submitted. Users having a high degree of similarity regarding their ratings are termed near neighbors, and in order to formu-late a recommendation for a user, her near neighbors{\textquoteright} ratings are extracted and form the basis for the recommendation. Col-laborative filtering algorithms however exhibit the problem commonly referred to as {\textquotedblleft}gray sheep{\textquotedblright}: this pertains to the case where for some users no near neighbors can be identified, and hence no personalized recommendations can be computed. The {\textquotedblleft}gray sheep{\textquotedblright} problem is more severe in sparse datasets, i.e. datasets where the number of ratings is small, compared to the number of items and users. In this paper, we address the {\textquotedblleft}gray sheep{\textquotedblright} problem by introducing the concept of virtual near neighbors and a related algorithm for their creation on the basis of the existing ones. We evaluate the proposed algorithm, which is termed as CFVNN, using eight widely used datasets and consid-ering two correlation metrics which are widely used in Collabo-rative Filtering research, namely the Pearson Correlation Coef-ficient and the Cosine Similarity. The results show that the pro-posed algorithm considerably leverages the capability of a Col-laborative Filtering system to compute personalized recommen-dations in the context of sparse datasets, tackling thus efficiently the {\textquotedblleft}gray sheep{\textquotedblright} problem. In parallel, the CFVNN algorithm achieves improvements in rating prediction quality, as this is expressed through the Mean Absolute Error and the Root Mean Square Error metrics.},
	keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Sparse Datasets, Virtual Near Neighbors},
	author = {Dionisis Margaris and Dionysios Vasilopoulos and Costas Vassilakis and Dimitris Spiliotopoulos}
}
@conference {margarisDatacom2018a,
	title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Accuracy by Considering Users{\textquoteright} Rating Variability},
	booktitle = {2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech)},
	year = {2018},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {When rating predictions are computed in user-user collaborative filtering, each individual rating is typically adjusted by the mean of the ratings entered by the specific user. This practice takes into account the fact that users follow different rating practices, in the sense that some are stricter when rating items, while others are more lenient. However, users{\textquoteright} rating practices may also differ in rating variability, in the sense that some user may be entering ratings close to her mean, while another user may be entering more extreme ratings, close to the limits of the rating scale. In this work, we (1) propose an algorithm that considers users{\textquoteright} ratings variability in the rating prediction computation process, aiming to improve rating prediction quality and (2) evaluate the proposed algorithm against seven widely used datasets considering three widely used variability measures and two user similarity metrics. The proposed algorithm, using the {\textquotedblleft}mean absolute deviation around the mean{\textquotedblright} variability measure, has been found to intro-duce considerable gains in rating prediction accuracy, in every dataset and under both user similarity metrics tested.},
	keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Users{\textquoteright} Ratings Variability},
	doi = {10.1109/DASC/PiCom/DataCom/CyberSciTec.2018.00145},
	url = {https://ieeexplore.ieee.org/document/8512012},
	author = {Dionisis Margaris and Costas Vassilakis}
}
