## 3 The Proposed Algorithm
On the individual dataset level, the density increase achieved by the proposed algorithm ranges from 24 times (initial dataset density: 0.118%; density of DR-enriched dataset: 2.834%), for the Amazon \"Amazon Grocery and Gourmet Food\" dataset, to 369 times for the Amazon \"Movies and TV\" dataset (initial dataset density: 0.027%; density of DR-enriched dataset: 9.953%). Furthermore, we can clearly notice that, after the application of the CF\({}_{\text{DR}}\) algorithm preprocessing step, all the datasets, even the more sparse ones (such as the Amazon \"CDs and Vinyl\", having an initial density of 0.023%) exceed the density threshold of 1% set by [56], beyond which a dataset D is considered to be dense. Correspondingly, under the CS as the similarity metric, again, the incorporation of DRs increases the average density from 0.6% that the initial datasets averaged, to 25.77%. On the individual dataset

Figure 4: Harmonic Mean of the normalized accuracy and coverage increase measures for the candidate setting, when using the CS as the similarity metric. level, the density increase achieved by the proposed algorithm ranges from 31 times for the MovieLens \"100 K\" dataset (initial dataset density: 3.17%; density of DR-enriched dataset: 99.7%), to 671 times for the Amazon \"Movies and TV\" dataset (initial dataset density: 0.027%; density of DR-enriched dataset: 18.12%). #### 4.2.2 Prediction Coverage Increase

In this subsection, we present the detailed results on the performance evaluation of the algorithm in terms of rating prediction coverage, using the seven datasets listed in \[\text{HM(s)}=2*\frac{normCovInc(s)\times normAccInc(s)}{normCovInc(s)+normAccInc (s)} \tag{6}\]0 In this experiment, the performance of the plain CF algorithm is used as a yardstick. Besides obtaining absolute metrics on coverage improvement by the CF\({}_{\text{DR}}\) algorithm presented in this paper, we perform a detailed comparison between its performance and the performance of the following algorithms:

1. The CF\({}_{\text{VNN}}\) algorithm [27], where artificial user profiles (virtual near neighbours--VNNs) are created by combining pairs of real users which are NNs of high similarity. The CF\({}_{\text{VNN}}\) algorithm was configured to use the optimal parameters for its operation reported in [27], i.e., _Th(sim)_ = 1.0 and _Th(cr)_ = 1. For more details on the parameters, the interested reader is referred to [27]. 2. The CF\({}_{\text{FOAF}}\) algorithm [29], where the transitivity of the NN relationship is explored, and two users can be considered as NNs with each other if they are NNs to a third user. The CF\({}_{\text{FOAF}}\) algorithm was configured to use the optimal parameters for its operation reported in [29], i.e., _Th(sim, src)_ = 0, _Th(sim, targ)_ = 0, _Th(sim, endpoints)_ = 0, _Th(cr, src)_ = 1, _Th(cr, targ)_ = 1, and _Th(cr, comb)_ = 2. For more details on the parameters, the interested reader is referred to [29]. Both the aforementioned algorithms (a) are state-of-the-art (the CF\({}_{\text{VNN}}\) algorithm [27] was presented in 2019, while the CF\({}_{\text{FOAF}}\) algorithm was presented in 2020) algorithms targeting primarily to increase the CF rating prediction coverage and also to alleviate the \"grey sheep\" problem, (b) achieve considerable improvement in coverage while additionally attaining slight improvement on rating prediction accuracy, and (c) necessitate no supplementary data, such as user relationships obtained from social networks, item categories, etc. The rationale behind the choice of the CF\({}_{\text{VNN}}\) and CF\({}_{\text{FOAF}}\) algorithms, for the performance comparison, is that among all CF algorithms targeting coverage increase without necessitating additional data, the first algorithm is proven to be better at rating prediction accuracy, while the second

\[\text{HM(s)}=2*\frac{normCovInc(s)\times normAccInc(s)}{normCovInc(s)+normAccInc (s)} \tag{6}\]1 Dataset density before and after the insertion of DR in the rating database. one achieves higher rating prediction coverage increments.