Recommendation systems (RS systems) has been popular among research communities as they are contributing significantly to the e-commerce domain. This kind of systems helps entrepreneurs to introduce their products to prospective customers and they also help online shoppers find the products that might somehow satisfy their needs and preferences. Many different techniques and RS models were proposed with the intention to introduce products that mostly satisfy user’s preferences. In other words, the models that can improve performances of recommendation systems are significant. In this article, semantic similarity was employed in addition to Cosine similarity to measure the similarity between two users in RS models. K-Nearest Neighbor (KNN) was utilized to determine the most similar users, which are important to retrieve the most satisfied products. The comparison between RS models that employed semantic similarity and the RS model that does not take semantic similarity factor into consideration was conducted over the benchmark datasets MovieLens 100K. For system evaluation, Mean Absolute Error (MAE), Root Means Square Error (RMSE) and the Cohen’s kappa (KAPPA) metrics were used to evaluate the RS models. The comparisons between two groups of models including RS systems with regularized information and RS systems without regularized information were conducted. The experiment results show that the RS model that employed Word Embedding method as regularized information provided higher performances than the other RS systems.