@article {797,
	title = {On Addressing the Low Rating Prediction Coverage in Sparse Datasets Using Virtual Ratings},
	journal = {SN Computer Science},
	volume = {2},
	year = {2021},
	month = {May},
	pages = {255},
	abstract = {<p>Collaborative filtering-based recommendation systems consider users{\textquoteright} likings and interests, articulated as ratings within a database to offer personalized recommendations. Unfortunately, many collaborative filtering datasets exhibit the {\textquoteleft}{\textquoteleft}grey sheep{\textquoteright}{\textquoteright} phenomenon, a state where no near neighbours can be found for certain users. This phenomenon is extremely frequent in datasets where users, on average, have rated only a small percentage of the available items, which are termed as sparse datasets. This paper addresses the {\textquoteleft}{\textquoteleft}grey sheep{\textquoteright}{\textquoteright} problem by proposing the virtual ratings concept and introduces an algorithm for virtual rating creation on the basis of actual ratings. The novelty behind this concept is that the introduction of the virtual ratings effectively reduces the user{\textendash}item rating matrix sparsity, thus alleviating the aforementioned problem. The proposed algorithm, which is termed as CFVR, has been extensively evaluated and the results show that it achieves to considerably improve the capability of a collaborative filtering system to formulate tailored recommendations for each user, when operating on sparse datasets, while at the same time improves rating prediction quality.</p>
},
	keywords = {collaborative filtering, Cosine Similarity, Evaluation, Pearson Correlation Coefficient, Recommender Systems, Sparse Datasets, Virtual ratings},
	issn = {2661-8907},
	doi = {10.1007/s42979-021-00668-8},
	url = {https://doi.org/10.1007/s42979-021-00668-8},
	author = {Margaris, Dionisis and Spiliotopoulos, Dimitris and Karagiorgos, Gregory and Vassilakis, Costas and Vasilopoulos, Dionysios}
}
@article {722,
	title = {Handling uncertainty in social media textual information for improving venue recommendation formulation quality in social networks},
	journal = {Social Network Analysis and Mining},
	volume = {9},
	year = {2019},
	month = {Oct},
	pages = {64},
	abstract = {One of the major problems that social media front is to continuously produce successful, user-targeted information, in the form of recommendations, which are produced by applying methods from the area of recommender systems. One of the most important applications of recommender systems in social networks is venue recommendation, targeted by the majority of the leading social networks (Facebook, TripAdvisor, OpenTable, etc.). However, recommender systems{\textquoteright} algorithms rely only on the existence of numeric ratings which are typically entered by users, and in the context of social networks, this information is scarce, since many social networks allow only reviews, rather than explicit ratings. Even if explicit ratings are supported, users may still resort to expressing their views and rating their experiences through submitting posts, which is the predominant user practice in social networks, rather than entering explicit ratings. User posts contain textual information, which can be exploited to compute derived ratings, and these derived ratings can be used in the recommendation process in the lack of explicitly entered ratings. Emerging recommender systems encompass this approach, without however tackling the fact that the ratings computed on the basis of textual information may be inaccurate, due to the very nature of the computation process. In this paper, we present an approach which extracts features of the textual information, a widely available source of information in venue category, to compute a confidence metric for the ratings that are computed from texts; then, this confidence metric is used in the user similarity computation and venue rating prediction formulation process, along with the computed rating. Furthermore, we propose a venue recommendation method that considers the generated venue rating predictions, along with venue QoS, similarity and spatial distance metrics in order to generate venue recommendations for social network users. Finally, we validate the accuracy of the rating prediction method and the user satisfaction from the recommendations generated by the recommendation formulation algorithm. Conclusively, the introduction of the confidence level significantly improves rating prediction accuracy, leverages the ability to generate personalized recommendations for users and increases user satisfaction.},
	keywords = {collaborative filtering, Confidence level, Rating prediction, Recommender Systems, Social Networks, Textual information, Uncertainty, Venue recommendation formulation},
	issn = {1869-5469},
	doi = {10.1007/s13278-019-0610-x},
	url = {https://doi.org/10.1007/s13278-019-0610-x},
	author = {Margaris, Dionisis and Vassilakis, Costas and Spiliotopoulos, Dimitris}
}
