## 5 Conclusion and Future Work  
In this work, we have presented a novel CF algorithm that considers the information of the black sheep ratings between NNs in the CF rating prediction procedure for the improvement of the rating prediction accuracy. More specifically, a set of black sheep ratings between two NNs appears when they both like a generally unaccepted item (they both give a relatively high rating when compared to the relatively low average rating given by all database users for this item) or vice versa. The rationale behind the use of the aforementioned concept is derived from the fact that if a human likes an item (e.g., a TV series, a car model, a videogame) in the real world, that the majority of others do not (and hence this product obtains a relatively low average rating value), and this human finds another one that also likes the exact same product (which is a quite rare case), the probability of valuing his opinion with greater prominence than the opinions of other users, for a future recommendation, is relatively high. We have experimentally validated the proposed algorithm through a set of experiments, using two user similarity metrics, namely the PCC and the CS (which are the two most used user similarity metrics in CF research [77; 78; 79]), two rating prediction error metrics, namely the MAE and the RMSE, and six datasets of diverse product categories (videogames, music, books, movies and TV series) to ensure the reliability and generalisability of the results. Furthermore, the proposed algorithm was tested both as a standalone application and combined with another CF algorithm also aiming at enhancing rating prediction accuracy [54]. The evaluation results have shown that significant prediction accuracy gains were introduced through the inclusion of the proposed algorithm. In the first case (standalone application) an average of 2% rating prediction error reduction was found, considering all cases. In the second case (when combined with another CF algorithm) the inclusion of the proposed algorithm achieved a further average rating prediction error relative reduction of 16%. Lastly, regarding the time and space complexity of the proposed algorithm, it was shown that both overheads are small. An identified limitation of this work is that datasets in which the majority of the user ratings are close to the mean value of the rating scale could potentially limit the gains that can be reaped by the algorithm. Our future work will initially focus on addressing the aforementioned limitation. Furthermore, we plan to explore alternative methods for rating prediction error reduction in CF datasets, in general. Finally, we will examine the extension of the proposed algorithm so that it can include additional data sources, such IoT data [80, 81, 82], social network-sourced information [44, 83, 84], and demographic features [85, 86, 87], targeting at further improving rating prediction accuracy. Conceptualization, D.M., D.S. and C.V.; methodology, D.M., D.S. and C.V.; software, D.M., D.S. and C.V.; validation, D.M., D.S. and C.V.; formal analysis, D.M., D.S. and C.V.; investigation, D.M., D.S. and C.V.; resources, D.M., D.S. and C.V.; data curation, D.M., D.S. and C.V.; writing--original draft preparation, D.M., D.S. and C.V.; writing--review and editing, D.M., D.S. and C.V.; visualization, D.M., D.S. and C.V.; supervision, D.M., D.S. and C.V.; project administration, D.M., D.S. and C.V. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. [0.5 cm]  
**Infrared Consent Statement:** Not applicable. [0.5 cm]  
**Data Availability Statement:** Publicly available datasets were analysed in this study.