## 2 Related Work  
The accuracy of CF-based recommender systems is a research field that has attracted numerous research works over the last years, which are divided into two main categories. The first category includes research works which exploit supplementary sources of information, such as user relations in social networks and item characteristics, while the second category includes research works that are based solely on the information contained in the user-item rating matrix. In regard to the first category, [41] examines the impact of incorporating social ties in the prediction formulation, targeting prediction accuracy and presents a social network CF algorithm which tunes the contribution of the social information by using a learning method as a weight parameter in the proposed similarity measure. The work in [42] extracts information from distant social relations and captures opinions from users while modelling their interactions with items, introducing a deep social CF algorithm that exploits social network information for recommendation production. Ref. [43] proposes a method that overcomes the cold start problem and the data sparsity in CF, by designing a Matrix Factorization (MF) Linked Open Data model, which uses a knowledge base to find information concerning the new entities. Ref. [44] states that both group affiliation information and social network information may significantly enhance the accuracy of popularity-based voting recommender systems as well as introduce a set of NN-based and MF-based recommender systems for online social voting. The work in [45] combines tie strength with social network information to create a local random walk-based friend recommendation method. Initially, the basis for friend recommendation is constructed, by using a weighted friend network and then this network is used to compute user similarity by a local random walk-based similarity measure. Ref. [46] firstly introduces a sparsity alleviation approach, based on implicit and explicit satisfaction and uses objective and subjective trust, to establish enhanced trust relationships among users. Then, for each target user, it selects the user's trusted neighbours, which are screened using emotional consistency. Finally, it predicts item ratings to obtain the final recommendations lists. Ref. [36] combines time decay factor for rated items, cognition relationships between users,and personal cognition behaviour into a unified probabilistic MF model and presents a social MF method for personalised recommendation using social interaction factors. Although all the previous works achieve relatively high rating prediction accuracy improvement, the supplementary source of information required may not always be available. As a result, an algorithm which can work using only the information located in the user-item-rating matrix may prove to be more appropriate, since it can be applied to every CF dataset. To this extent, ref. [47] introduces an approach that realises an item-variance weighting in item-based CF. More specifically, it applies a time-related correlation degree to form time-aware similarity computation, which estimates the relationship between two items and reduces the importance of an item that has not recently been rated. Ref. [48] presents a CF optimization method that initially incorporates multiple interests to optimize neighbour selection and then it utilises a ranking strategy that rearranges both the top-N item list and the area the threshold controls, maximising the popularity while maintaining a relatively low prediction accuracy reduction. Ref. [49] clusters items and users by using a Gaussian mixture model and builds a new interaction matrix by extracting new item features that manages to solve the impact of rating data sparsity on CF algorithms. Furthermore, by combining the Jaccard and triangle similarities, it proposes a new similarity calculation algorithm. Ref. [50] presents a slope one algorithm based on user similarity and trusted data fusion that can be applied in various CF systems.