Hybrid Movie Recommender System based on Resource Allocation

Recommender Systems are inevitable to personalize user's experiences on the Internet. They are using different approaches to recommend the Top-K items to users according to their preferences. Nowadays recommender systems have become one of the most important parts of largescale data mining techniques. In this paper, we propose a Hybrid Movie Recommender System (HMRS) based on Resource Allocation to improve the accuracy of recommendation and solve the cold start problem for a new movie. HMRS-RA uses a self-organizing mapping neural network to clustering the users into N clusters. The users' preferences are different according to their age and gender, therefore HMRS-RA is a combination of a Content-Based Method for solving the cold start problem for a new movie and a Collaborative Filtering model besides the demographic information of users. The experimental results based on the MovieLens dataset show that the HMRS-RA increases the accuracy of recommendation compared to the state-of-art and similar works.

FNHSM_HRS: Hybrid recommender system using fuzzy clustering and heuristic similarity measure

Nowadays, Recommender Systems have become a comprehensive system for helping and guiding users in a huge amount of data on the Internet. Collaborative Filtering offers to active users based on the rating of a set of users. One of the simplest and most comprehensible and successful models is to find users with a taste in recommender systems. In this model, with increasing number of users and items, the system is faced to scalability problem. On the other hand, improving system performance when there is little information available from ratings, that is important. In this paper, a hybrid recommender system called FNHSM_HRS which is based on the new heuristic similarity measure (NHSM) along with a fuzzy clustering is presented. Using the fuzzy clustering method in the proposed system improves the scalability problem and increases the accuracy of system recommendations. The proposed system is based on the collaborative filtering model and is partnered with the heuristic similarity measure to improve the system's performance and accuracy. The evaluation of the proposed system based results on the MovieLens dataset carried out the results using MAE, Recall, Precision and Accuracy measures Indicating improvement in system performance and increasing the accuracy of recommendation to collaborative filtering methods which use other measures to find similarities.