In this thesis the problem of providing good recommendations to assist users to make the best choice out of numerous options is studied. To overcome the common problem of sparsity of the data, from which recommendations are inferred, additional label information assigned to items is considered. Based on a literature survey approaches that have proven to perform well were identified and combined into a single Framework. The proposed framework is based on the third-order Restricted Boltzmann machine which enables to incorporate label information as well as traditional rating information into a single model. The framework also implements the global-approach of collaborative filtering, where the user- and item-based approaches are both considered to improve the performance of the model. The proposed framework is implemented and evaluated using an experiment measuring the prediction error on test samples. The results obtained from the conducted experiments did not confirm the assumptions made about the improve of the models accuracy, when incorporating the additional label information. Reasons for this are identified and discussed.