Association rules are used in recommender systems to develop a model that enhances the profiles of users, as well as to address the cold start problem. Our approach proposes a model which is implemented in a system for recommending scientific papers called Collaborative Topic Regression (CTR). Collaborative Topic Regression consists of two matrices, U and V, where U represents the relationship between users and paper topics, while V represents the relationship between papers and the paper

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... . The CTR model is focused on matrix V, by adopting it, and as the result, it is influenced by the paper's textual content, leaving the content of matrix U essentially unchanged. The depicted model extracts association rules from matrix U, and then enriches it, with the information gleaned from the mining process. The outcomes are based on both, out-of-matrix prediction and in-matrix prediction. Our approach improved the quality of the results by up to 20% for out-of-matrix prediction in the best-case scenario. Unfortunately, the same cannot be said for in-matrix prediction, which will be further investigated in the future.