###### Abstract  
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' ratings on items, is presented. This accuracy enhancement is achieved by augmenting the importance of the opinions of 'black sheep near neighbours', 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. collaborative filtering; black sheep users; rating prediction accuracy; evaluation +
Footnote †: journal: Medical  
handling, scalability and high accuracy and (c) the hybrid approach exhibits elevated performance and overcome sparsity and loss of information [3; 8; 9; 10]. Correspondingly, each of the three approaches exhibit a number of disadvantages: (a) the memory-based approach cannot adequately handle sparsity, does not scale well with the volume of data, and number of entities and falls behind in accuracy; (b) the model-based approach is time-consuming to build and update, cannot be directly applied on a diverse user range, and has low explainability, and (c) the hybrid approach exhibits increased complexity and high implementation cost. Explainability has been recently recognized as a key aspect in recommender systems. Explainable recommender systems are able to show to their users comprehensible descriptions on why an item is recommended, thus increasing transparency and the likelihood that recommendations are accepted [11; 12; 13]. In this area, model-based approaches exhibit considerably lower performance than memory-based approaches, since it is very hard to explain how deep learning networks (which are predominantly used to implement model-based approaches) have amassed their knowledge, on which the recommendations are based [11]. Under this view, an enterprise or organization may opt to employ a memory-based recommender system, aiming to improve the probability that recommendations are accepted while in parallel lowering the recommender system creation and maintenance cost, albeit the accuracy of recommendations may not be optimal. Moreover, a number of techniques have been developed for memory-based systems, aiming to tackle the deficiencies presented above; these include distributed techniques [14] and optimization methods [15; 16; 17; 18] to improve scalability, density enrichment [19] and coverage increase methods [20] to tackle sparsity, and a multitude of methods to improve rating prediction and recommendation quality [21; 22; 23; 24; 25]. Considering all the above, memory-based techniques are a viable approach for building contemporary and efficient recommender systems. Memory-based CF algorithms, as the one presented in this work, typically, include three main steps: (i) find users having similar or at least close tastes, by examining the similarity of already submitted ratings, (ii) predict the rating value that a user would give to an item that he or she has not evaluated yet, and (iii) recommend the items having the highest prediction values to the (target) user [26].