Hybrid Movie Recommender System based on Resource Allocation
This work addresses the problem of enhancing movie recommendations for users, particularly in handling new items, but it appears incremental as it builds on existing hybrid approaches.
The paper tackles improving recommendation accuracy and solving the cold start problem for new movies by proposing a hybrid movie recommender system based on resource allocation, which combines content-based methods, collaborative filtering, and demographic information, and experimental results on the MovieLens dataset show increased accuracy compared to state-of-the-art methods.
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.