Building a mobile movie recommendation service by user rating and APP usage with linked data on Hadoop

Movie recommendation systems are important tools that suggest films with respect to users’ choices through item-based collaborative filter algorithms, and have shown positive effect on the provider’s revenue. Given that mobile Apps are rapidly growing, the recommender is implemented to support web s...

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Bibliographic Details
Published in:Multimedia tools and applications 2017-02, Vol.76 (3), p.3383-3401
Main Authors: Hsieh, Meng-Yen, Chou, Wen-Kuang, Li, Kuan-Ching
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Applications programs
Cold
Collaboration
Computer Communication Networks
Computer Science
Data processing
Data Structures and Information Theory
Employment
Feedback
Internet service providers
Linked Data
Motion pictures
Multimedia Information Systems
Ratings
Ratings & rankings
Recommender systems
Smartphones
Social networks
Sparsity
Special Purpose and Application-Based Systems
Studies
Systems analysis
User generated content
Web services
Web site design
Wireless communications
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Summary:Movie recommendation systems are important tools that suggest films with respect to users’ choices through item-based collaborative filter algorithms, and have shown positive effect on the provider’s revenue. Given that mobile Apps are rapidly growing, the recommender is implemented to support web services in frontend Apps. Among those films recommended, users can give ratings and feedback, collecting film information from linked data concurrently. In order to solve cold-start problems, Cluster-based Matrix Factorization is adopted to model user implicit ratings related to Apps usage. Knowing that user rating data processing is a large-scale problem in producing high quality recommendations, MapReduce and NoSQL environments are employed in performing efficient similarity measurement algorithms whilst maintaining rating and film datasets. In this investigation, the system analyzes user feedbacks to evaluate the recommendation accuracy through metrics of precision, recall and F-score rates, while cold-start users make use the system with two MovieLens datasets as main rating reference in the recommendation system.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-016-3833-0