Recommender systems cannot provide healthy results in case of similar products that cannot be identified in e-commerce sites. Insufficient information about users or items is one of the most crucial problems, especially with adding new users or products. The inability to perform relational analysis in the system is due to insufficient data. In this case, the system cannot recommend or bring the non-related items to the users. This chapter suggests the gray relational approach to identify more healthy recommendation lists when there are few relational items. The data was obtained from an e-commerce company and apriori algorithm was applied to the dataset that a randomly chosen user purchased. Gray relational analysis was applied for the most suitable recommendation by using support, confidence, number of likes, adding favorite, deleting from basket, and return information of the products in the dataset. In addition, the most appropriate product sequencing of the recommendation list was realized by gray relational degrees.
An Extended Hybrid Recommender System Based on Association Rules Mining in Discussion Groups
