Efficient Distributed Matrix Factorization Alternating Least Squares (EDMFALS) for Recommendation Systems Using Spark

2021 ◽  
Author(s):  
R. R. S. Ravi Kumar ◽  
G. Appa Rao ◽  
S. Anuradha
Keyword(s):  
Least Squares ◽  
Real Time ◽  
Data Streams ◽  
High Speed ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Alternating Least Squares ◽  
Time Data ◽  
Distributed Framework ◽  
User Ratings

With the emergence of e-commerce and social networking systems, the use of recommendation systems gained popularity to predict the user ratings of an item. Since the large volume of data is generated from various sources at high speed, predicting the ratings accurately in real-time adds enormous benefit to the users while choosing the correct item. So a recommendation system must be capable enough to predict the rating accurately when the data are large. Apache Spark is a distributed framework well suited for processing large datasets and real-time data streams. In this paper, we propose an efficient matrix factorisation algorithm based on Spark MLlib alternating least squares (ALS) for collaborative filtering. The optimisations used for the proposed algorithm using Tungsten improved the performance of the algorithm significantly while doing the predictions. The experimental results prove that the proposed work is significantly faster for top-N recommendations and rating predictions compared with the existing works.

Windows High-Speed Drawing Technology Research

2013 ◽  
Vol 441 ◽  
pp. 660-665 ◽  
Author(s):  
Zhen Dong Chou
Keyword(s):  
Real Time ◽  
High Speed ◽  
Technology Research ◽  
Time Data ◽  
Real Time Data ◽  
Real Time Data Processing ◽  
Graphics Engine ◽  
Realtime System ◽  
Huge Challenge ◽  
Drawing Method

The display speed of image and large real-time data processing is a huge challenge for realtime system. This paper completed a thorough research on existing drawing technology on the platform of windows; analyzed adaptive characteristics of using the general high-speed drawing techniques for high speed drawing and its merits and demerits. Finally, through a lot of experiments and simulations of high speed drawing process after optimization and combination, tested their drawing performance and efficiency in order to select an appropriate drawing method to develop a high-speed graphics engine for large real-time data.

scholarly journals Movie Recommenadation System

2021 ◽  
Vol 9 (4) ◽  
pp. 13-16
Author(s):  
Gandhali Malve ◽  
Lajree Lohar ◽  
Tanay Malviya ◽  
Shirish Sabnis
Keyword(s):  
Machine Learning ◽  
Recommender System ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Recommendation Systems ◽  
Machine Learning Algorithms ◽  
The Internet ◽  
Amount Of Information ◽  
System Recommendation ◽  
User Ratings

Today the amount of information in the internet growth very rapidly and people need some instruments to find and access appropriate information. One of such tools is called recommendation system. Recommendation systems help to navigate quickly and receive necessary information. Many of us find it difficult to decide which movie to watch and so we decided to make a recommender system for us to better judge which movie we are more likely to love. In this project we are going to use Machine Learning Algorithms to recommend movies to users based on genres and user ratings. Recommendation system attempt to predict the preference or rating that a user would give to an item.

Knowledge Discovery From Evolving Data Streams

2019 ◽  
pp. 19-39
Author(s):  
Prasanna Lakshmi Kompalli
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Data Stream ◽  
Concept Drift ◽  
Data Stream Mining ◽  
Time Data ◽  
Stream Mining ◽  
New Challenges ◽  
Mining Data Streams ◽  
Different Sources

Data coming from different sources is referred to as data streams. Data stream mining is an online learning technique where each data point must be processed as the data arrives and discarded as the processing is completed. Progress of technologies has resulted in the monitoring these data streams in real time. Data streams has created many new challenges to the researchers in real time. The main features of this type of data are they are fast flowing, large amounts of data which are continuous and growing in nature, and characteristics of data might change in course of time which is termed as concept drift. This chapter addresses the problems in mining data streams with concept drift. Due to which, isolating the correct literature would be a grueling task for researchers and practitioners. This chapter tries to provide a solution as it would be an amalgamation of all techniques used for data stream mining with concept drift.

Data Reduction Techniques for Near Real-Time Decision Making in Fall Prediction Systems

2018 ◽  
pp. 52-64
Author(s):  
Masoud Hemmatpour ◽  
Renato Ferrero ◽  
Filippo Gandino ◽  
Bartolomeo Montrucchio ◽  
Maurizio Rebaudengo
Keyword(s):  
Real Time ◽  
High Speed ◽  
Time Data ◽  
Reduction Techniques ◽  
Fall Prediction ◽  
Real Time Data ◽  
Data Volume ◽  
Prediction Systems ◽  
Unintentional Falls ◽  
Health Service Costs

Unintentional falls are a frequent cause of hospitalization that mostly increases health service costs due to injuries. Fall prediction systems strive to reduce injuries and provide fast help to the users. Typically, such systems collect data continuously at a high speed through a device directly attached to the user. Whereas such systems are implemented in devices with limited resources, data volume is significantly important. In this chapter, a real-time data analyzer and reducer is proposed in order to manage the data volume of fall prediction systems.

Monitoring Elite Soccer Players’ External Loads Using Real-Time Data

2017 ◽  
Vol 12 (10) ◽  
pp. 1285-1287 ◽  
Author(s):  
Steve Barrett
Keyword(s):  
Real Time ◽  
High Speed ◽  
Training Session ◽  
Maximum Velocity ◽  
Soccer Players ◽  
Data Sets ◽  
Time Data ◽  
Electrical Systems ◽  
Locomotor Activities ◽  
Physical Output

Purpose: To assess the validity of measuring locomotor activities and PlayerLoad using real-time (RT) data collection during soccer training. Methods: Twenty-nine English soccer players participated. Each player wore the same MEMS device (Micromechanical Electrical Systems; S5, Optimeye; CatapultSports, Melbourne, Australia) during 21 training sessions (N = 331 data sets) in the 2015–16 and 2016–17 seasons. An RT receiver (TRX; Catapultsports, Melbourne, Australia) was used to collect the locomotor activities and PlayerLoad data in RT and compared with the postevent downloaded (PED) data. PlayerLoad and locomotor activities (total distance covered; total high-speed running distance covered, >5.5#x00A0;m/s; total sprinting distance covered, >7 m/s; maximum velocity) were analyzed. Results: Correlations were near perfect for all variables analyzed (r = .98–1.00), with a varied level of noise between RT and PED also (0.3–9.7% coefficient of variation). Conclusions: Locomotor activities and PlayerLoad can use both RT and PED concurrently to quantify a player’s physical output during a training session. Caution should be taken with higher-velocity-based locomotor activities during RT compared to PED.

Sign in / Sign up

Export Citation Format

Share Document