Recommender Systems has grown significantly over the last two decades. Memory-based Collaborative Filtering is part of RS and is a powerful technology that has been applied in several well-established commercial applications.However, memory-based collaborative filtering fails to capture the dynamic user opinions in a detailed perceptive since it uses a two-dimensional rating approach.However, multicriteria RS dominates memory-based collaborative filtering with the inclusion of multiple contexts. In addition, significant research has been done to predict user gratification. However, recent multicriteria recommender systemsfail to avoid the significant issues of the curse of dimensionalitydue to the lower number of ratings among multiple dimensions, leading to poor predictions. This paper proposes a new prediction recommender model on multicriteria recommender systems to predict user gratification with the memory-based user and item collaborative filtering approaches used to impute the missing contextsin multicriteria RS. In addition, various regression models were applied to overall and predicted overall ratings. The results indicate that item-item collaborative filtering with Ordinary Least Squares(OLS) regression in multicriteria RS exhibits low Root Mean Squared Error(RMSE), indicating the accurate predictions of user gratification.