@inbook {808,
	title = {Identifying Reliable Recommenders in Users{\textquoteright} Collaborating Filtering and Social Neighbourhoods},
	booktitle = {Lecture Notes in Social Networks},
	year = {2021},
	pages = {51{\textendash}76},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {<p>Recommender systems increasingly use information sourced from social networks to improve the quality of their recommendations. However, both recommender systems and social networks exhibit phenomena under which information for certain users or items is limited, such as the cold start and the grey sheep phenomena in collaborative filtering systems and the isolated users in social networks. In the context of a social network-aware collaborative filtering, where the collaborating filtering- and social network{\textendash}based neighbourhoods are of varying density and utility for recommendation formulation, the ability to identify the most reliable recommenders from each neighbourhood for each user and appropriately combine the information associated with them in the recommendation computation process can significantly improve the quality and accuracy of the recommendations offered. In this chapter, we report on our extensions on earlier works in this area which comprise of (1) the development of an algorithm for discovering the most reliable recommenders of a social network recommender system and (2) the development and evaluation of a new collaborative filtering algorithm that synthesizes the opinions of a user{\textquoteright}s identified recommenders to generate successful recommendations for the particular user. The proposed algorithm introduces significant gains in rating prediction accuracy (4.9\% on average, in terms of prediction MAE reduction and 4.2\% on average, in terms of prediction RMSE reduction) and outperforms other algorithms. The proposed algorithm, by design, utilizes only basic information from the collaborative filtering domain (user{\textendash}item ratings) and the social network domain (user relationships); therefore, it can be easily applied to any social network recommender system dataset.</p>
},
	keywords = {collaborative filtering, Evaluation, Limited Information, Near Neighbours, Rating prediction, Recommender Systems, Social Networks},
	doi = {10.1007/978-3-030-67044-3_3},
	author = {Dionisis Margaris and Dimitris Spiliotopoulos 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 {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}
}
