Collaborative filtering-based recommendation system for big data

2020 ◽  
Vol 21 (2) ◽  
pp. 219 ◽  
Author(s):  
Jian Shen ◽  
Tianqi Zhou ◽  
Lina Chen
Keyword(s):  
Big Data ◽  
Collaborative Filtering ◽  
Recommendation System

Big Data Mining Using Collaborative Filtering

Web Services ◽  
2019 ◽  
pp. 702-711
Author(s):  
Anu Saini
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Big Data Mining ◽  
Detailed Survey ◽  
Past Behavior

Today every big company, like Google, Flipkart, Yahoo, Amazon etc., is dealing with the Big Data. This big data can be used to predict the recommendation for the user on the basis of their past behavior. Recommendation systems are used to provide the recommendation to the users. The author presents an overview of various types of recommendation systems and how these systems give recommendation by using various approaches of Collaborative Filtering. Various research works that employ collaborative filtering for recommendations systems are reviewed and classified by the authors. Finally, this chapter focuses on the framework of recommendation system of big data along with the detailed survey on the use of the Big Data mining in collaborative filtering.

scholarly journals A Recommendation Engine for Predicting Movie Ratings Using a Big Data Approach

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1215
Author(s):  
Mazhar Javed Awan ◽  
Rafia Asad Khan ◽  
Haitham Nobanee ◽  
Awais Yasin ◽  
Syed Muhammad Anwar ◽  
...  
Keyword(s):  
Big Data ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Robust Clustering ◽  
Search Data ◽  
Filtering Technique ◽  
Streaming Services ◽  
Movie Recommendation ◽  
Movie Ratings ◽  
Als Algorithm

In this era of big data, the amount of video content has dramatically increased with an exponential broadening of video streaming services. Hence, it has become very strenuous for end-users to search for their desired videos. Therefore, to attain an accurate and robust clustering of information, a hybrid algorithm was used to introduce a recommender engine with collaborative filtering using Apache Spark and machine learning (ML) libraries. In this study, we implemented a movie recommendation system based on a collaborative filtering approach using the alternating least squared (ALS) model to predict the best-rated movies. Our proposed system uses the last search data of a user regarding movie category and references this to instruct the recommender engine, thereby making a list of predictions for top ratings. The proposed study used a model-based approach of matrix factorization, the ALS algorithm along with a collaborative filtering technique, which solved the cold start, sparse, and scalability problems. In particular, we performed experimental analysis and successfully obtained minimum root mean squared errors (oRMSEs) of 0.8959 to 0.97613, approximately. Moreover, our proposed movie recommendation system showed an accuracy of 97% and predicted the top 1000 ratings for movies.

Big Data Mining Using Collaborative Filtering

2017 ◽  
pp. 128-138
Author(s):  
Anu Saini
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Big Data Mining ◽  
Detailed Survey ◽  
Past Behavior

Today every big company, like Google, Flipkart, Yahoo, Amazon etc., is dealing with the Big Data. This big data can be used to predict the recommendation for the user on the basis of their past behavior. Recommendation systems are used to provide the recommendation to the users. The author presents an overview of various types of recommendation systems and how these systems give recommendation by using various approaches of Collaborative Filtering. Various research works that employ collaborative filtering for recommendations systems are reviewed and classified by the authors. Finally, this chapter focuses on the framework of recommendation system of big data along with the detailed survey on the use of the Big Data mining in collaborative filtering.

scholarly journals The application of e-commerce recommendation system in smart cities based on big data and cloud computing

2021 ◽  
pp. 26-26
Author(s):  
Yiman Zhang
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Collaborative Filtering ◽  
Real Time ◽  
Evaluation System ◽  
Recommendation System ◽  
Smart Cities ◽  
Personalized Recommendation ◽  
Current Recommendation ◽  
Recommendation Algorithm

In the era of big data, the amount of Internet data is growing explosively. How to quickly obtain valuable information from massive data has become a challenging task. To effectively solve the problems faced by recommendation technology, such as data sparsity, scalability, and real-time recommendation, a personalized recommendation algorithm for e-commerce based on Hadoop is designed aiming at the problems in collaborative filtering recommendation algorithm. Hadoop cloud computing platform has powerful computing and storage capabilities, which are used to improve the collaborative filtering recommendation algorithm based on project, and establish a comprehensive evaluation system. The effectiveness of the proposed personalized recommendation algorithm is further verified through the analysis and comparison with some traditional collaborative filtering algorithms. The experimental results show that the e-commerce system based on cloud computing technology effectively improves the support of various recommendation algorithms in the system environment; the algorithm has good scalability and recommendation efficiency in the distributed cluster, and the recommendation accuracy is also improved, which can improve the sparsity, scalability and real-time problems in e-commerce personalized recommendation. This study greatly improves the recommendation performance of e-commerce, effectively solves the shortcomings of the current recommendation algorithm, and further promotes the personalized development of e-commerce.

The Application of Cloud Based on Latent Factor Algorithm in Personalized Recommendation

2013 ◽  
Vol 411-414 ◽  
pp. 2288-2291
Author(s):  
Jian Xi Peng ◽  
Zhi Yuan Liu
Keyword(s):  
Big Data ◽  
Collaborative Filtering ◽  
Real Time ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Massive Data ◽  
Latent Factor ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Data Environment

Recommendation system is a commercial marketing method. What more, the system could increase adhesion and satisfaction of consumers to the website which brings great commercial benefit to electronic commerce. But with big data ages coming, it makes a great challenge to real-time recommendation system. As for latent factor class collaborative filtering algorithm, a distributed constructed latent factor algorithm based on cloud is presented in this paper. The algorithm could keep collaborative filtering in good recommendation and ensure the real time in massive data environment. The simulation shows that the algorithm could achieve the recommendation efficiently and quickly. High speedup and scalability are proved.

scholarly journals A Big Data Analysis Method Based on Modified Collaborative Filtering Recommendation Algorithms

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 966-974
Author(s):  
Nan Yin
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Collaborative Filtering ◽  
Execution Time ◽  
Recommendation System ◽  
Big Data Analysis ◽  
Analysis Method ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

Abstract With the rapid development of e-commerce, collaborative filtering recommendation system has been widely used in various network platforms. Using recommendation system to accurately predict customers’ preferences for goods can solve the problem of information overload faced by users and improve users’ dependence on the network platform. Because the recommendation system based on collaborative filtering technology has the ability to recommend more abstract or difficult to describe goods in words, the research related to collaborative filtering technology has attracted more and more attention. According to the past research, in collaborative filtering algorithm, if Pearson correlation coefficient is used, errors will occur under special circumstances. In this study, the normal recovery similarity measure is used to modify the similarity value to correct the error value of a collaborative filtering recommendation algorithm. Based on this, a big data analysis method based on a modified collaborative filtering recommendation algorithm is proposed. This research implemented it in the cloud Hadoop environment, and measure the execution time with 2, 5 and 8 nodes. Then the research compared it with the execution time of a single machine, and analyze its speedup ratio and efficiency. The experimental results show that the execution time increases with the number of neighbors. When the number of nodes is 5 and 8, the execution time is greatly improved, which improves the efficiency of collaborative filtering algorithm and can cope with massive data in the future.

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