Development of a recommendation model to support decision-making when the user chooses products

Recommender systems are used to predict user preferences for a particular product or service, and to recommend suitable products or services to the user. Many of the methods used in data mining, related to classification or the construction of association rules, are used in recommender systems. This article proposes a new recommender model that combines association rules and statistical implication index measures. In the proposed model, support and confidence measures are used to create association rules, and the statistical implication index measure is used to filter the set of rules and rank recommendations. The proposed model and algorithms are used to build a recommendation result based on a known data set.