The personalized recommender systems provide favorite services based on user preferences and interests. Due to the user’s interests changing over time; hence the recommender system must be tracking these changes automatically; to overcome the research gap and col start problem in the current study, we suggest a framework to create an adaptive user profiling for a personalized recommender system using learning automata. We clustered items based on their features. In this technique, the learning automaton adjusts the amount of user interest in each cluster based on user feedback; then recommends the best items to the user based on demographic information of user and user’s preferences. Several experiments are conducted on three movie datasets to show the performance of the proposed algorithm. The obtained results demonstrate that the proposed algorithm outperforms several existing approaches in terms of precision, recall, MAE, and RMSE.
