A Hybrid Recommender System for Improving Rating Prediction of Movie Recommendation

Because of COVID-19 pandemic, online movies are now extremely popular. While the movie theaters have not serviced and people are staying quarantine, movies are the best choice for relaxing and treating stress. In present, recommender systems are widely integrated into many platforms of movie applications. A hybrid recommender system is one promising technique to improve the system performance, especially for cold-start, data sparsity, and scalability. This paper proposed a hybrid of matrix factorization, biased matrix factorization, and factor wise matrix factorization to solve all mentioned drawback problems. Simulation shows that the proposed hybrid algorithm can decrease approximately 11.91% and 10.70% for RMSE and MAE, respectively, when compared with the traditional methods. In addition, the proposed algorithm is capable of scalability. While the number of datasets is tremendously increased by 10 times, it is still effectively executed. © 2022 IEEE.

A Survey of Learning-Based Methods for Cold-Start, Social Recommendation, and Data Sparsity in E-commerce Recommendation Systems

With the continuous development of the economy and technology, people more and more rely on online shopping, especially during the pandemic of COVID19. On the other hand, sellers display many products, so customers need to make a great effort to find suitable products to meet their needs. To reduce the efforts of customers, researchers have developed many recommendation systems for online products. In this paper, to help further study recommendation systems in e-commerce, we survey the learning-based methods for solving the cold-start problem in a recommendation, social recommendation, and data sparsity. In particular, we compare these methods' pros and cons and point out the directions for further study. © 2021 IEEE.

Hybrid and Ensemble-Based Personalized Recommender System - Solving Data Sparsity Problem

Online content streaming is the most popular form of entertainment in recent times due to COVID 19 lockdown. All popular streaming services use various product recommendation schemes to retain users to their services by intriguing them with content that they might like. Various recommendation systems have been used by famous streaming services like Netflix, Amazon Prime, Hulu, etc. but they lack consistency and accuracy as they suffer from some severe problems such as the first rater problem, sparsity problem, and various computations problems. In this research, we have come up with a hybrid machine learning recommender system which uses an ensemble of content-based and collaborative filtering techniques to not only solve all data sparsity problems but also provide more personalized recommendations to the users based on their watching history and user profile. This research provides a new algorithm that increases the quality of content that is being recommended to the users. © 2021 IEEE.