Improving Recommendation Techniques by Deep Learning and Large Scale Graph Partitioning
Journal Title: International Journal of Advanced Computer Science & Applications - Year 2018, Vol 9, Issue 10
Abstract
Recommendation is very crucial technique for social networking sites and business organizations. It provides suggestions based on users’ personalized interest and provide users with movies, books and topics links that would be most suitable for them. It can improve user effectiveness and business revenue by approximately 30%, if analyzed in intelligent manner. Social recommendation systems for traditional datasets are already analyzed by researchers and practitioners in detail. Several researchers have improved recommendation accuracy and throughput by using various innovative approaches. Deep learning has been proven to provide significant improvements in image processing and object recognition. It is machine learning technique where hidden layers are used to improve outcome. In traditional recommendation techniques, sparsity and cold start are limitations which are due to less user-item interactions. This can be removed by using deep learning models which can improve user-item matrix entries by using feature learning. In this paper, various models are explained with their applications. Readers can identify best suitable model from these deep learning models for recommendation based on their needs and incorporate in their techniques. When these recommendation systems are deployed on large scale of data, accuracy degrades significantly. Social big graph is most suitable for large scale social data. Further improvements for recommendations are explained with the use of large scale graph partitioning. MAE (Mean Absolute Error) and RMSE (Root Mean Squared Error) are used as evaluation parameters which are used to prove better recommendation accuracy. Epinions, MovieLens and FilmTrust datasets are also shown as most commonly used datasets for recommendation purpose.
Authors and Affiliations
Gourav Bathla, Rinkle Rani, Himanshu Aggarwal
Keywords
Related Articles
- EP ID EP408213
- DOI 10.14569/IJACSA.2018.091049
- Views 79
- Downloads 0
Student Facial Authentication Model based on OpenCV’s Object Detection Method and QR Code for Zambian Higher Institutions of Learning
Facial biometrics captures human facial physiological data, converts it into a data item variable so that this stored variable may be used to provide information security services, such as authentication, integrity manag...
Performance Evaluation of Routing Protocol (RPL) for Internet of Things
Recently, Internet Engineering Task Force (IETF) standardized a powerful and flexible routing protocol for Low Power and Lossy Networks (RPL). RPL is a routing protocol for low power and lossy networks in the Internet of...
Intruder Attacks on Wireless Sensor Networks: A Soft Decision and Prevention Mechanism
Because of the wide-ranging of applications in a variety of fields, such as medicine, environmental studies, robotics, warfare and security, and so forth, the research on wireless sensor networks (WSNs) has attracted muc...
An Enhanced Method for Detecting the Shaded Images of the Car License Plates based on Histogram Equalization and Probabilities
Shadow is one of the major and significant challenges in detection algorithms which track the objects such as the license plates. The quality of images captured by cameras is influenced by weather conditions, low ambient...
A Review of Towered Big-Data Service Model for Biomedical Text-Mining Databases
The rapid growth of biomedical informatics has drawn increasing popularity and attention. The reason behind this are the advances in genomic, new molecular, biomedical approaches and various applications like protein ide...