A Hybrid Recommender System for Improving Rating Prediction of Movie Recommendation
19th International Joint Conference on Computer Science and Software Engineering, JCSSE 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2018936
ABSTRACT
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.
cold-start problem; collaborative filtering system; data sparsity; hybrid recommender system; movies; recommender system; scalability; Collaborative filtering; Matrix algebra; Matrix factorization; Motion pictures; Recommender systems; Best choice; Cold start problems; Cold-start; Collaborative filtering systems; Hybrid recommender systems; Matrix factorizations; Movie; Movie recommendations; Systems performance
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
19th International Joint Conference on Computer Science and Software Engineering, JCSSE 2022
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS