A Novel Method for Improving Cold Start Challenge in Recommender Systems through Users Demographics Information

The user cold start challenge, occurs when a user joins the system which used recommender systems, for the first time. Since the recommender system has no knowledge of the user preferences at first, it will be difficult to make appropriate recommendations. In this paper, users’ demographics information are used for clustering to find the users with similar preferences in order to improve the cold start challenge by employing the kmeans, k-medoids, and k-prototypes algorithms. The target user’s neighbors are determined by using a hybrid similarity measure including a combination of users’ demographics information similarity and users rating similarity. The asymmetric Pearson correlation coefficient utilized to calculate the user rating similarity, whereas GMR (i.e., global most rated) and GUC(i.e., global user local clustering) strategies are adopted to make recommendations. The proposed method was implemented on MovieLens dataset. The results of this research shows that the MAE of the proposed method has improved the accuracy of the proposals up to about 26% compared to the GMR method and up to about 34% compared to the GUC method. Also, the results show about 60% improvement in terms of rating coverage compared to the GMR and GUC methods.