@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}
}
@article {margaris2018a,
	title = {Exploiting Rating Abstention Intervals for Addressing Concept Drift in Social Network Recommender Systems},
	journal = {Informatics},
	volume = {5},
	number = {2},
	year = {2018},
	month = {apr},
	pages = {21 pages},
	abstract = {One of the major problems that social networks face is the continuous production of successful, user-targeted information in the form of recommendations, which are produced exploiting technology from the field of recommender systems. Recommender systems are based on information about users{\textquoteright} past behavior to formulate recommendations about their future actions. However, as time goes by, social network users may change preferences and likings: they may like different types of clothes, listen to different singers or even different genres of music and so on. This phenomenon has been termed as concept drift. In this paper: (1) we establish that when a social network user abstains from rating submission for a long time, it is a strong indication that concept drift has occurred and (2) we present a technique that exploits the abstention interval concept, to drop from the database ratings that do not reflect the current social network user{\textquoteright}s interests, thus improving prediction quality.},
	keywords = {collaborative filtering, concept drift, Evaluation, Recommender Systems, shift of interest, Social Networks},
	doi = {10.3390/informatics5020021},
	url = {http://www.mdpi.com/2227-9709/5/2/21},
	author = {Dionisis Margaris and Costas Vassilakis}
}
@article {margaris-csimq-2017,
	title = {Using Time Clusters for Following Users{\textquoteright} Shifts in Rating Practices},
	journal = {Complex Systems Informatics and Modeling Quarterly},
	volume = {13},
	year = {2017},
	abstract = {Users that enter ratings for items follow different rating practices, in the sense that, when rating items, some users are more lenient, while others are stricter. This aspect is taken into account by the most widely used similarity metric in user-user collaborative filtering, namely, the Pearson Correlation, which adjusts each individual user rating by the mean value of the ratings entered by the specific user, when computing similarities. However, a user{\textquoteright}s rating practices change over time, i.e. a user could start as strict and subsequently become lenient or vice versa. In that sense, the practice of using a single mean value for adjusting users{\textquoteright} ratings is inadequate, since it fails to follow such shifts in users{\textquoteright} rating practices, leading to decreased rating prediction accuracy. In this work, we address this issue by using the concept of dynamic averages introduced earlier and we extend earlier work by (1) introducing the concept of rating time clusters and (2) presenting a novel algorithm for calculating dynamic user averages and exploiting them in user-user collaborative, filtering implementations. The proposed algorithm incorporates the aforementioned concept and is able to follow more successfully shifts in users{\textquoteright} rating practices. It has been evaluated using numerous datasets, and has been found to introduce significant gains in rating prediction accuracy, while outperforming the dynamic average computation approaches that are presented earlier.},
	keywords = {collaborative filtering, Dynamic Average, Rating Abstention Interval, Rating Time Clusters, Ratings{\textquoteright} Timestamps, Recommender Systems},
	issn = {2255-9922},
	doi = {10.7250/csimq.2017-13.02},
	author = {Dionisis Margaris and Costas Vassilakis}
}
