## 2 Related Work  
CF-based RS prediction accuracy is a field that has attracted significant research efforts over the last years [30; 31; 32]. In contrast, research in CF-based RS coverage is less developed. [PERSON] et al. [33] present ItemHyCov, which is a CF-based prediction algorithm that combines the strengths of user-based and item-based CF approaches into a feature combination hybrid CF approach, targeting to upgrade the CF rating prediction coverage in sparse CF datasets. In order to achieve its goals, the ItemHyCov algorithm introduces a preprocessing hybrid combination step. However, it necessitates both extra preprocessing time and extra storage space, as well as continuous updates. The CF\({}_{\text{negNNs}}\) algorithm presented in [26] increases the CF rating prediction coverage in sparse datasets, by incorporating, in the rating prediction computation process, users with negative similarity to the user for whom the rating prediction is computed. [PERSON] et al. [27] propose the CF\({}_{\text{NN}}\) algorithm, which creates virtual user profiles, termed virtual NNs (VNNs), by merging pairs of NN profiles corresponding to real users having high similarity. The introduction of the VNN profiles contributes to the alleviation of the \"grey sheep\" problem. Additionally, [PERSON] and [PERSON] [29] present an algorithm, termed CF\({}_{\text{FOAF}}\), which adopts the friend-of-a-friend concept in CF, in the sense that two users can be considered as NNs with each other if they are NNs to a third user. Effectively, the CF\({}_{\text{FOAF}}\) algorithm explores the effect of considering transitivity of the NN relationships, examining different parameters of computing properties of the derived relationships. [PERSON] et al. [24] present a clustering CF recommendation algorithm that exploits social relationships between social network (SN) users to identify their social neighbourhoods. It applies a complex network clustering technique on the SN users to formulate similar user groups and, then, uses traditional CF algorithms to generate the recommendations. [PERSON] et al. [34] combine CF techniques with user trust relationships and put forward the recommendation approach based on trust delivery, in order to solve the data sparsity and cold-start problems that the sparse CF datasets suffer from. [PERSON] et al. [35] propose an algorithm which fuses trusted rating data (i.e., rating data that have been upvoted by community users as \"helpful\") with user similarity and, subsequently, applies an improved version of the slope one algorithm to generate rating predictions. The proposed algorithm comprises three procedures, namely the selection of trusted data, similarity calculation between users, and weight factor addition to the aforementioned similarity, in order to create the recommendation equation. [PERSON] and [PERSON] [36] propose a social memory-based CF algorithm and examine the impact of integrating social ties on both prediction coverage and accuracy. Moreover, in order to tune the contribution of SN information, the proposed algorithm incorporates a learning method as a weight parameter in the proposed similarity measure. [PERSON] et al. [37] propose an algorithm that targets SN RSs that operate using sparse user-item rating matrices, as well as sparse social graphs. For each user-item prediction, the proposed algorithm computes partial predictions for item ratings from both the user's CF neighbourhood and SN neighbourhood, which are then combined using a weighted average technique. The weight is adjusted to fit the characteristics of the dataset. For users that have no CF or SN NNs, item predictions are based only on the respective user's social neighbourhood or CF neighbourhood, respectively. All the aforementioned approaches significantly improve CF rating prediction coverage. However, the improvement is based on information derived from complementary information sources (e.g., a SN), which is not always available.