A Proposed Model to Solve Cold Start Problem using Fuzzy User-Based Clustering

In online environments, a vast amount of data is explored daily on the internet, such as news, movies, audios, and books. The interest of the target user is the greatest demand for recommender systems, and getting suitable information is a challenge. To develop a recommender system, collaborative filtering (CF) approach considers users who have similar ratings. Therefore, can compute the similarity of users when there are enough rated by users to items. A significant challenge of the collaborative filtering approach is cold start problem, which is how to make a recommendation for users who have few ratings than others. This work proposes a collaborative filtering model based on applying fuzzy c-means clustering on user's truthfulness information. A new fuzzy user-based similarity measure formula is suggested which combines user's rating with fuzzy truthfulness information using a combination coefficient. The experimental results using Movie Lens data set have shown an improvement in recommendation under sparsity and cold start condition.