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.

The Research and Application of MapReduce Based Neighbor Model in Personalized Recommendation

2013 ◽  
Vol 373-375 ◽  
pp. 1674-1677
Author(s):  
Jian Xi Peng ◽  
Zhi Yuan Liu
Keyword(s):  
Collaborative Filtering ◽  
Real Time ◽  
Recommendation System ◽  
High Efficiency ◽  
Personalized Recommendation ◽  
Massive Data ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Computing Framework ◽  
Model Algorithm

Personalized recommendation provides convenience to users and brings more benefit to companies as well. It has been an important part of electronic commerce website. Collaborative filtering is a common algorithm in recommendation system. But with massive data ages coming, traditional collaborative filtering algorithm could not finish recommendation in time. A neighbor model algorithm based on MapReduce distributed computing framework is presented against to collaborative filtering algorithm. The presented algorithm could accomplish the personalized recommendation effectively and meet the real time requirement completely. The simulation shows that the algorithm has high efficiency and could complete the recommended in a highly efficient and real-time.

A Collaborative Filtering Recommendation Algorithm Combined with User and Item

2014 ◽  
Vol 513-517 ◽  
pp. 1878-1881
Author(s):  
Feng Ming Liu ◽  
Hai Xia Li ◽  
Peng Dong
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Experimental Results ◽  
Personalized Recommendation ◽  
User Evaluation ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm

The collaborative filtering recommendation algorithm based on user is becoming the more personalized recommendation algorithm. But when the user evaluation for goods is very small and the user didnt evaluate the item, the commodity recommendation based on the item evaluation of user may not be accurate, and this is the sparseness in the collaborative filtering algorithm based on user. In order to solve this problem, this paper presents a collaborative filtering recommendation algorithm based on user and item. The experimental results show that this method has smaller MAE and greatly improve the quality of the recommendation in the recommendation system.

scholarly journals An Improved Collaborative Filtering Recommendation Algorithm and Recommendation Strategy

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Xiaofeng Li ◽  
Dong Li
Keyword(s):  
Collaborative Filtering ◽  
Community Detection ◽  
Recommendation System ◽  
Detection Algorithm ◽  
Personalized Recommendation ◽  
Filtering Algorithm ◽  
Collaborative Recommendation ◽  
Target User ◽  
Collaborative Filtering Algorithm ◽  
Community Detection Algorithm

The e-commerce recommendation system mainly includes content recommendation technology, collaborative filtering recommendation technology, and hybrid recommendation technology. The collaborative filtering recommendation technology is a successful application of personalized recommendation technology. However, due to the sparse data and cold start problems of the collaborative recommendation technology and the continuous expansion of data scale in e-commerce, the e-commerce recommendation system also faces many challenges. This paper has conducted useful exploration and research on the collaborative recommendation technology. Firstly, this paper proposed an improved collaborative filtering algorithm. Secondly, the community detection algorithm is investigated, and two overlapping community detection algorithms based on the central node and k-based faction are proposed, which effectively mine the community in the network. Finally, we select a part of user communities from the user network projected by the user-item network as the candidate neighboring user set for the target user, thereby reducing calculation time and increasing recommendation speed and accuracy of the recommendation system. This paper has a perfect combination of social network technology and collaborative filtering technology, which can greatly increase recommendation system performance. This paper used the MovieLens dataset to test two performance indexes which include MAE and RMSE. The experimental results show that the improved collaborative filtering algorithm is superior to other two collaborative recommendation algorithms for MAE and RMSE performance.

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.

scholarly journals Design of personalized recommendation system for online learning resources based on improved collaborative filtering algorithm

2020 ◽  
Vol 214 ◽  
pp. 01051
Author(s):  
Baiqiang Gan ◽  
Chi Zhang
Keyword(s):  
Online Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Online Course ◽  
Learning Resources ◽  
Filtering Algorithm ◽  
Collaborative Filtering Algorithm ◽  
Open Courses ◽  
Online Learning Resources

In recent years, under the guidance of the educational concept of equality and sharing, universities at home and abroad have increased the development and application of online course learning resources. In China, online open courses are open to all learners on the platform of major portals. Due to the increasing number of online courses, it is increasingly difficult for learners to find the content they are interested in on the website. In addition, the traditional collaborative filtering has the problems of sparse data, cold start, and low accuracy of recommendation results, etc. Therefore, the personalized recommendation system studied in this paper adds the collaborative filtering recommendation technology of user and project attributes. The recommendation system can actively discover the interest of learners according to their behavior characteristics, and provide them with online learning resources of interest, and improve the accuracy of the recommendation results by improving the collaborative filtering algorithm. In this paper, personalized recommendation technology is applied to online course website, aiming at providing personalized, automated and intelligent recommendation system for online learners.

scholarly journals Design of a Personalized Recommendation System for Learning Resources based on Collaborative Filtering

2022 ◽  
Vol 16 ◽  
pp. 122-131
Author(s):  
Mingxia Zhong ◽  
Rongtao Ding
Keyword(s):  
Online Learning ◽  
Collaborative Filtering ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Learning Resources ◽  
Learning Behaviors ◽  
Filtering Algorithm ◽  
Learning Platform ◽  
Collaborative Filtering Algorithm ◽  
Learning Platforms

At present, personalized recommendation system has become an indispensable technology in the fields of e-commerce, social network and news recommendation. However, the development of personalized recommendation system in the field of education and teaching is relatively slow with lack of corresponding application.In the era of Internet Plus, many colleges have adopted online learning platforms amidst the coronavirus (COVID-19) epidemic. Overwhelmed with online learning tasks, many college students are overload by learning resources and unable to keep orientation in learning. It is difficult for them to access interested learning resources accurately and efficiently. Therefore, the personalized recommendation of learning resources has become a research hotspot. This paper focuses on how to develop an effective personalized recommendation system for teaching resources and improve the accuracy of recommendation. Based on the data on learning behaviors of the online learning platform of our university, the authors explored the classic cold start problem of the popular collaborative filtering algorithm, and improved the algorithm based on the data features of the platform. Specifically, the data on learning behaviors were extracted and screened by knowledge graph. The screened data were combined with the collaborative filtering algorithm to recommend learning resources. Experimental results show that the improved algorithm effectively solved the loss of orientation in learning, and the similarity and accuracy of recommended learning resources surpassed 90%. Our algorithm can fully satisfy the personalized needs of students, and provide a reference solution to the personalized education service of intelligent online learning platforms.

Research on Goods Recommendation Strategy Based on Decision Tree

2014 ◽  
Vol 687-691 ◽  
pp. 2718-2721
Author(s):  
Jie Gao
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Test Results ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Different Types ◽  
Noise Data ◽  
Collaborative Filtering Algorithm ◽  
Recommendation Strategy

Firstly, associative-sets-based collaborative filtering algorithm is proposed. During the process of personalized recommendation, some items evaluated by users are performed by accident, in other words, they have little correlation with users' real preferences. These irrelevant items are equal to noise data, and often interfere with the effectiveness of collaborative filtering. A personalized recommendation algorithm based on Associative Sets is proposed in this paper to solve this problem. It uses frequent it sets to get associative sets, and makes recommendations according to users' real preferences, so as to enhance the accuracy of recommending results. Test results show that the new algorithm is more accurate than the traditional. Secondly, a flexible E-Commerce recommendation system is built. Traditional recommendation system is a sole tool with only one recommendation model. In e-commerce environment, commodities are very rich, personal demands are diversification; E-Commerce systems in different occasions require different types of recommended strategies. For that, we analysis the recommendation system with flexible theory, and proposed a flexible e-commerce recommendation system. It maps the implementation and demand through strategy module, and the whole system would be design as standard parts to adapt to the change of the recommendation strategy.

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.

The Improved Collaborative Filtering Recommendation Algorithm Based on Cloud Model

2013 ◽  
Vol 411-414 ◽  
pp. 2292-2296
Author(s):  
Jia Si Gu ◽  
Zheng Liu
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Cloud Model ◽  
Personalized Recommendation ◽  
Test Results ◽  
User Interest ◽  
Recommendation Algorithm ◽  
Filtering Algorithm ◽  
Model Based ◽  
Collaborative Filtering Algorithm

The traditional collaborative filtering algorithm has a better recommendation quality and efficiency, it has been the most widely used in personalized recommendation system. Based on the traditional collaborative filtering algorithm,this paper considers the user interest diversity and combination of cloud model theory.it presents an improved cloud model based on collaborative filtering recommendation algorithm.The test results show that, the algorithm has better recommendation results than other kinds of traditional recommendation algorithm.

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