Recommender systems have been developed to serve users and provide them with the best suggestions. Despite their success, offering fully identical recommendations to users’ preferences remains a difficult task, where the complexity of human taste results in different challenges. Grey sheep user phenomena continues to be one of the most common, where the user is defined by his unique interactions with the system, making it difficult to associate with similar users, as he rarely agrees with them. In this study, we presented a new approach for identifying grey sheep users. It is based on the taste context and nature of user interaction with the system. We grouped similar users using an enhanced Kmedoids clustering method with a new dissimilarity metric and introduced a novel process to distinguish between users. The differentiation is achieved by assigning weights to each cluster based on how much it reflects the grey sheep user characteristics. We evaluated the efficiency of Grey Threshold Medoids (GTMedoids) using the FilmTrust and MovieLens 100k datasets. The results show the superior performance of our approach in detecting grey sheep users.
Boualaoui et al. (Thu,) studied this question.