Model-driven approach running route two-level SVD with context information and feature entities in recommender system

In digital businesses, the offers of goods and services to users in recommendation systems are generally based on the features of the items and the demands of the users. Recommendation systems use contextual information, such as location, time, activity, individual, and relationship to generate relevant and even personalized recommendations. A large proportion of users do not always rate items that they have used which creates the challenge of sparse data in recommendation systems. On the other hand, when a new user or item enters the system, it does not have much information about it. This leads to the challenge a cold start. In this paper, a new hybrid method is presented to reduce the challenge of cold start and sparse data based on the items desired by users according to the similarity measure of user-contextual information, item-contextual information, and two-level singular value decomposition. Two data sets IMDB and STS due to the exerting of user's feature, items feature, and contextual information to review the proposed method. In order to the accuracy of the prediction of the criterion MAE and RMSE with an accuracy of 95%. However, since the user's rating of the item is of particular importance in the recommender systems. The method CSSVD compared to the methods TF, CACF, CTLSVD, and MF-LOD is used evaluation measure Precision, Recall, F1-score. The results contribute to improving the accuracy of the suggestions given to users in recommender systems.