@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 {716,
	title = {Improving Collaborative Filtering{\textquoteright}s Rating Prediction Quality by Exploiting the Item Adoption Eagerness Information},
	booktitle = {Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence},
	year = {2019},
	publisher = {ACM},
	organization = {ACM},
	abstract = {Collaborative filtering computes personalized recommendations by taking into account ratings expressed by users. Collaborative filtering algorithms firstly identify people having similar tastes, who are termed as {\textquotedblleft}near neighbors" and recommendations for a user, are based on his near neighbors{\textquoteright} ratings. On the other hand, people exhibit different levels of eagerness to adopt new products: according to this characteristic, there is a set of users, termed as {\textquotedblleft}Early Adopters", who are prone to start using a product or technology as soon as it becomes available, in contrast to the majority of users, who prefer to start using items once they reach maturity; this important aspect of user behavior is not taken into account by existing algorithms. In this work, we (1) propose an algorithm that considers the eagerness shown by users to adopt products, aiming to improve rating prediction quality and (2) evaluate the proposed algorithm against seven widely used datasets.},
	keywords = {collaborative filtering, Cosine Similarity, Evaluation, item adoption eagerness, Pearson Correlation Coefficient, rating prediction quality},
	isbn = {978-1-4503-6934-3},
	doi = {10.1145/3350546.3352544},
	author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis}
}
@conference {Margaris_2019_asonam,
	title = {Social Relations versus Near Neighbours: Reliable Recommenders in Limited Information Social Network Collaborative Filtering for Online Advertising},
	booktitle = {Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019)},
	year = {2019},
	month = {aug},
	abstract = {Online advertising benefits by recommender systems since the latter analyse reviews and rating of products, providing useful insight of the buyer perception of products and services. When traditional recommender system information is enriched with social network information, more successful recommendations are produced, since more users{\textquoteright} aspects are taken into consid-eration. However, social network information may be unavaila-ble since some users may not have social network accounts or may not consent to their use for recommendations, while rating data may be unavailable due to the cold start phenomenon. In this paper, we propose an algorithm that combines limited col-laborative filtering information, comprised only of users{\textquoteright} ratings on items, with limited social network information, com-prised only of users{\textquoteright} social relations, in order to improve (1) prediction accuracy and (2) prediction coverage in collaborative filtering recommender systems, at the same time. The proposed algorithm considerably improves rating prediction accuracy and coverage, while it can be easily integrated in recommender systems.},
	keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Online Advertising, Pearson Correlation Coefficient, Social Networks},
	author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis}
}
