Tavsiye sistemi için veri madenciliği tekniklerini kullanarak veri seyrekliğini ve soğuk başlatma sorunlarını hafifletmek için çeşitli modeller

The development of Web 2.0 and the rapid growth of available data have led to the evolution of multiple systems among them the Recommendation Systems (RSs) which can handle the information overload. Due to the fact that, RSs performance is substantially limited by sparsity and cold-start problems; this research study takes the route for a major objective to attenuate these problems. To realize this objective, four data mining techniques are proposed namely: multi-steps resource allocation- singular value decomposition (MSRA-SVD), MSRA-SVD++, clustering community detection, and overlapping community detection. The first two models are dedicated to tackle the data sparsity problem whereas the rest models are adopted to alleviate cold-start users' problem. The core strategy of the first two models is to use the (MSRA) method to identify hidden relations in social network. the MSRA method is applied to determine the probability of their relation. If the probability exceeds a threshold, a new relationship will be established. For the second model (MSRA-SVD++), an implicit feedback source is exploited as an additional source of information, which can be extracted via rating information. Additionally, clustering and overlapping models are adopted to overcome cold-start user problem. The main idea of these models is to apply a clustering technique to group users into several communities. In order to attain that, explicit and implicit social relations with the confidence values are integrated to compute distance values. Additionally, the partitioning around medoids (PAM) clustering algorithm is adopted. Later, for the last model, the average distances of all clusters are computed. For all users, the distance between users and the center node of a particular cluster is computed. If the distance is less than the average of this cluster, a new user for this cluster will be added. Moreover, the SVD++ method is employed for each cluster to compute the prediction value. The proposed models are evaluated through the usage of three real-world datasets. Ultimately, findings exhibited a great deal of insights on how the proposed models outperformed a number of the state-of-the-art studies in terms of prediction accuracy.