Numerous research studies have emphasized the significance of contextual information when it comes to recommender models. This importance is especially evident in the realm of e-commerce platforms, where recommender systems have been effectively suggesting products and services to users by integrating contextual data into their models. By doing so, these systems can better understand user preferences and behaviors during transactions on the platform. As a result, a growing number of platforms are now collecting evaluation values for products and services based on various user contexts, leading to the emergence of multi-context-based rating datasets. This presents a valuable opportunity to implement multi-criteria collaborative filtering models, which we propose as a solution. Our approach involves integrating user contextual rating data and conducting experiments using two sets of contextual evaluation datasets: De Paul Movie and In Car Music. The results demonstrate that the multi-criteria collaborative filtering model outperforms the single-context-based collaborative filtering model in terms of accuracy. This study opens up promising avenues for future research aimed at further enhancing recommendation accuracy for customers on online sales platforms.