###### Abstract  
Collaborative filtering algorithms formulate personalized recommendations for a user, first by analysing already entered ratings to identify other users with similar tastes to the user (termed as near neighbours), and then using the opinions of the near neighbours to predict which items the target user would like. However, in sparse datasets, too few near neighbours can be identified, resulting in low accuracy predictions and even a total inability to formulate personalized predictions. This paper addresses the sparsity problem by presenting an algorithm that uses robust predictions, that is predictions deemed as highly probable to be accurate, as derived ratings. Thus, the density of sparse datasets increases, and improved rating prediction coverage and accuracy are achieved. The proposed algorithm, termed as CF\({}_{\text{DR}}\), is extensively evaluated using (1) seven widely-used collaborative filtering datasets, (2) the two most widely-used correlation metrics in collaborative filtering research, namely the Pearson correlation coefficient and the cosine similarity, and (3) the two most widely-used error metrics in collaborative filtering, namely the mean absolute error and the root mean square error. The evaluation results show that, by successfully increasing the density of the datasets, the capacity of collaborative filtering systems to formulate personalized and accurate recommendations is considerably improved. recommender systems; collaborative filtering; sparse datasets; density enrichment; robust predictions; derived ratings; rating prediction coverage; rating prediction accuracy +
Footnote †: journal:  
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includes numerous research efforts aiming to tackle the data sparsity problem. To this end, most works exploit different types of additional information (i.e., information beyond the rating matrix), such as social relationships between users, item descriptions, textual reviews for items and so forth (a detailed review is given in Section 2). However, such information is not always available or exploitable (e.g., due to lack of user consent). This paper addresses the aforementioned problems by:  
1. Introducing the concepts of _derived ratings_ (DRs) and _robust predictions_: derived ratings are created from predictions with a high level of confidence regarding their accuracy, which are termed as robust predictions. DRs complement the explicitly entered ratings in the user-item rating matrix, increasing the matrix density. 2. Proposing an algorithm, termed \(CF_{DR}\), for the creation of DRs on the basis of real, explicit user ratings, while considering various prediction robustness criteria. 3. Extending the rating prediction computation algorithm to compute rating predictions on the complemented user-item rating matrix, while taking into account the level of confidence associated with each DR. The algorithm proposed in this paper outperforms all algorithms presented in the literature that target the data sparsity problem and rely only on the data included in the rating matrix, both in terms of being able to formulate personalized recommendations and increasing rating prediction accuracy. Additionally, the proposed approach can be combined with algorithms and techniques that exploit additional data and/or use different prediction computation approaches. To exemplify the concept of the CFDR algorithm, let us consider the case where we want to predict the rating \(r_{U3,i4}\) that user \(U_{3}\) would assign to item \(i_{4}\), in order to decide whether it should be recommended to him or not.