@conference {810,
	title = {Anchoring Effect Mitigation for Complex Recommender System Design},
	booktitle = {Proceedings of the 2022 HCI International Conference},
	year = {2022},
	keywords = {Information/knowledge design/visualization, mental-model, Metaphor, navigation design, Service Design},
	author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis}
}
@article {803,
	title = {Augmenting Black Sheep Neighbour Importance for Enhancing Rating Prediction Accuracy in Collaborative Filtering},
	journal = {Applied Sciences},
	volume = {11},
	year = {2021},
	month = {sep},
	pages = {8369},
	abstract = {<p>In this work, an algorithm for enhancing the rating prediction accuracy in collaborative filtering, which does not need any supplementary information, utilising only the users{\textquoteright} ratings on items, is presented. This accuracy enhancement is achieved by augmenting the importance of the opinions of {\textquoteleft}black sheep near neighbours{\textquoteright}, which are pairs of near neighbours with opinion agreement on items that deviates from the dominant community opinion on the same item. The presented work substantiates that the weights of near neighbours can be adjusted, based on the degree to which the target user and the near neighbour deviate from the dominant ratings for each item. This concept can be utilized in various other CF algorithms. The experimental evaluation was conducted on six datasets broadly used in CF research, using two user similarity metrics and two rating prediction error metrics. The results show that the proposed technique increases rating prediction accuracy both when used independently and when combined with other CF algorithms. The proposed algorithm is designed to work without the requirements to utilise any supplementary sources of information, such as user relations in social networks and detailed item descriptions. The aforesaid point out both the efficacy and the applicability of the proposed work.</p>
},
	keywords = {black sheep users, collaborative filtering, Evaluation, rating prediction accuracy},
	doi = {10.3390/app11188369},
	url = {https://doi.org/10.3390/app11188369},
	author = {Dionisis Margaris and Dimitris Spiliotopoulos and Costas Vassilakis}
}
@article {771,
	title = {An Adaptive Social Network-Aware Collaborative Filtering Algorithm for Improved Rating Prediction Accuracy},
	journal = {IEEE Access},
	volume = {8},
	year = {2020},
	pages = {68301{\textendash}68310},
	abstract = {When information from traditional recommender systems is augmented with information about user relationships that social networks store, more successful recommendations can be produced. However, this information regarding user relationships may not always be available, since some users may not consent to the use of their social network information for recommendations or may not have social network accounts at all. Moreover, the rating data (categories and characteristics of products) may be unavailable for a recommender system. In this paper, we present an algorithm that can be applied in any social network-aware recommender system that utilizes the users{\textquoteright} ratings on items and users{\textquoteright} social relations. The proposed algorithm addresses the issues of limited social network information or limited collaborative filtering information for some users by adapting its behavior, taking into account the density and utility of each user{\textquoteright}s social network and collaborative filtering neighborhoods. Through this adaptation, the proposed algorithm achieves considerable improvement in rating prediction accuracy. Furthermore, the proposed algorithm can be easily implemented in recommender systems},
	keywords = {Performance evaluation, Recommender Systems, Social computing},
	issn = {2169-3536},
	doi = {10.1109/access.2020.2981567},
	author = {Dionisis Margaris and Anna Kobusinska and Dimitris Spiliotopoulos and Costas Vassilakis}
}
@article {779,
	title = {An Algorithm for Density Enrichment of Sparse Collaborative Filtering Datasets Using Robust Predictions as Derived Ratings},
	journal = {Algorithms},
	volume = {13},
	year = {2020},
	month = {07/2020},
	pages = {174},
	abstract = {<p>Collaborative filtering algorithms formulate personalized recommendations for a user, first by analysing already entered ratings to identify other users with similar tastes to the user (termed as near neighbours), and then using the opinions of the near neighbours to predict which items the target user would like. However, in sparse datasets, too few near neighbours can be identified, resulting in low accuracy predictions and even a total inability to formulate personalized predictions. This paper addresses the sparsity problem by presenting an algorithm that uses robust predictions, that is predictions deemed as highly probable to be accurate, as derived ratings. Thus, the density of sparse datasets increases, and improved rating prediction coverage and accuracy are achieved. The proposed algorithm, termed as CFDR, is extensively evaluated using (1) seven widely-used collaborative filtering datasets, (2) the two most widely-used correlation metrics in collaborative filtering research, namely the Pearson correlation coefficient and the cosine similarity, and (3) the two most widely-used error metrics in collaborative filtering, namely the mean absolute error and the root mean square error. The evaluation results show that, by successfully increasing the density of the datasets, the capacity of collaborative filtering systems to formulate personalized and accurate recommendations is considerably improved.</p>
},
	keywords = {collaborative filtering, density enrichment, derived ratings, rating prediction accuracy, rating prediction coverage, Recommender Systems, robust predictions, Sparse Datasets},
	doi = {https://doi.org/10.3390/a13070174},
	author = {Dionisis Margaris and Dimitris Spiliotopoulos and Gregory Karagiorgos and Costas Vassilakis}
}
@conference {Margaris2013RCIS,
	title = {Adapting WS-BPEL scenario execution using collaborative filtering techniques},
	booktitle = {IEEE 7th International Conference on Research Challenges in Information Science (RCIS)},
	year = {2013},
	month = {may},
	publisher = {IEEE},
	organization = {IEEE},
	abstract = {WS-BPEL has been adopted as the predominant method for composing individual web services into higher-level business processes. The designers of WS-BPEL scenarios define at development time the specific web services that will be invoked in the context of the business process they model; in the context however of the current web, where each functionality is offered by multiple service providers, under different quality of service parameters, using a fixed BPEL scenario has been recognized to be inadequate for servicing the diverse needs of business processes clients. To this end, WS-BPEL scenario execution adaptation has been proposed, mainly allowing clients to specify quality of service policies, which drive the dynamic selection of the services that will be invoked. In this paper, we present a framework extending the quality of service-based adaptation mechanisms with collaborative filtering techniques, allowing clients to further refine the adaptation process by considering service selections made by other clients, in the context of the same business processes.},
	doi = {10.1109/rcis.2013.6577691},
	author = {Dionisis Margaris and P. Georgiadis and Costas Vassilakis}
}
