## 2 Related Work
Matrix factorization (MF) algorithms [38; 39] constitute an alternative category for computing rating predictions. However, since the MF-based approaches always produce a rating prediction when these algorithms are applied to sparse datasets, the predictions involving items or users having very few ratings actually degenerate to dataset-dependent constant values, which are not considered as personalized predictions. Furthermore, the rating prediction accuracy of these algorithms is relatively poor in these cases [40; 41]. [PERSON] et al. [42] address this issue by proposing an upgraded singular value decomposition (SVD) model that incorporates the MF algorithms with rating completion inspired by active learning, as well as a multi-layer ESVD model that further improves rating prediction accuracy. Moreover, [PERSON] et al. [43] present the benefits of using active learning techniques and the users' personalities and develop two extended MF algorithm versions to identify the items a user could and should rate, as well as compose personalized recommendations. [PERSON] et al. [44] propose a MF and a NN prediction technique for pairwise preference scores. The MF-based algorithm maps user and item pairs to a joint latent feature vector space, whereas the respective NN method employs custom user-to-user similarity functions, which are identified as more accurate for synthesizing user rating values into a user similarity metric. [PERSON] et al. [45] propose an approach for building a nonnegative latent factor (NLF) model, which is based on the alternating direction method. This method substitutes direct matrix manipulations by training the desired features through single-element-dependent optimisation, reaping considerable benefits in computational complexity and storage space, while also achieving accuracy improvement. [PERSON] et al. [46] present a framework that is able to tackle the data sparsity issue of CF by considering item-related emotions, extracted from user reviews on items, and semantic data, extracted from the movie plot summary. The presented framework relies on a Latent Dirichlet Allocation (LDA) extension and gradient boosted trees to predict the rating a user would give to a specific item. [PERSON] et al. [47] propose an algorithm that creates virtual ratings derived from SN users' textual reviews, by identifying opinion words existing in each review text, by mapping opinion words to sentiments, and aggregating sentiments into numeric rating values. [PERSON] et al. [48] present an algorithm that addresses the uncertainty inherent to the conversion of SN user textual reviews to numeric ratings by extracting and exploiting review text features, aiming to increase recommendation quality. [PERSON] and [PERSON] [49] propose a rating prediction algorithm that infers the user preferences from textual reviews of hotels, through sentiment analysis. Their proposed algorithm applies two approaches, \"memory-based CF\" and \"model-based CF\", where the preference scores obtained from the sentiment analysis are integrated into the rating prediction process. Although these approaches succeed in increasing the density of the user-item rating matrix and, thus, significantly increasing the rating prediction coverage, they cannot be applied when textual reviews are unavailable. None of the above mentioned works target density increase of sparse CF datasets, leveraging on the one hand the CF rating prediction coverage and, on the other hand, facilitating the application of techniques targeting at rating prediction accuracy, such as matrix factorization and pruning of old user ratings [39; 40; 50], which cannot be successfully applied in sparse CF datasets. The present paper advances the state-of-the-art for density enrichment of sparse CF datasets by introducing an algorithm that creates \(DRs\), using the robust predictions of the same (sparse) dataset'sratings, inserts them in the user-rating matrix as complementary ratings, and incorporates them in the prediction formulation process.