<![CDATA[The recommender systems are increasingly being more inrested in many fields of the life, it is increasingly effective in finding information to suggest to users in big data systems. The multi-criteria recommender systems are alwaysresearched and improved to suit the diverse requirements of data and userpreferences today. The calculation to make a reasonable decision is required for the multi-criteria consulting system. Many operations have been applied todecision making. Most traditional recommender systems often use average calculations to calculate useful values used in decision making. In this paper, we offer a new approach to develope decision-making based on interaction andnon-interaction between criteria. In an information system, data always hasinteractive relationships that represent intrinsic values in the system. If we do not add these values to calculate decision-making, the decision making willnot be complete. We build a multi-criteria consulting model in both Item-based(compare and evaluate results with some existing models) and User-based(Compare decision making operations) with non-interactive and interactivedecisions. Experiments show that when using interactive values, the results aremuch better, contributing to improving the quality of the current multi-criteria consulting system.]]>
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