Personalized Recommendation System of Web Academic Information Based on Big Data and Quality Monitoring Technology

2021 ◽  
pp. 263-274
Author(s):  
Jieli Sun ◽  
YanXia Zhao ◽  
Pei Liu ◽  
JianKe Li ◽  
Hao Wen Zhai
Keyword(s):  
Big Data ◽  
Recommendation System ◽  
Quality Monitoring ◽  
Personalized Recommendation ◽  
Monitoring Technology ◽  
Academic Information

scholarly journals Intelligent Tourism Personalized Recommendation Based on Multi-Fusion of Clustering Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
HongYan Liang
Keyword(s):  
Big Data ◽  
Recommendation System ◽  
Contextual Information ◽  
Clustering Algorithms ◽  
Personalized Recommendation ◽  
Information Usage ◽  
Practical Effect ◽  
Tourism Information ◽  
Travel Recommendation ◽  
Big Data Technology

Actual tourism mining models are often used to discover potential information in documents, but tourism models without human knowledge often produce unexplainable topics. This paper combines big data technology to build a personalized recommendation system for smart tourism, model the contextual information usage ontology under the tourism information system, and give the association between various ontologies. Then, this paper uses a matrix to describe each discrete attribute and interval attribute and uses a vector to model the user’s preferences. In addition, this paper constructs an intelligent recommendation system based on the actual needs of travel recommendation and verifies the system in combination with experimental research. Through experimental analysis, it can be known that the intelligent tourism personalized recommendation system based on big data technology proposed in this paper has a high practical effect.

scholarly journals Construction of E-commerce Personalized Information Recommendation System in the Era of Big Data

2021 ◽  
Vol 2074 (1) ◽  
pp. 012085
Author(s):  
Yaosheng Wang
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Service Recommendation ◽  
Actual System ◽  
Recommendation Algorithm ◽  
Dissimilarity Matrix ◽  
Logistics Service ◽  
Depth Study

Abstract With the continuous expansion of the scale of e-commerce, personalized recommendation technology has been widely used. However, the traditional recommendation system has been unable to meet the current needs of data processing, and good big data processing ability has become the basic requirement of the new personalized recommendation system. In addition, traditional recommendation systems are often limited to tangible goods recommendation, and pay less attention to e-commerce logistics service recommendation. In this paper, through the in-depth study of information personalized recommendation service in e-commerce environment, combined with the application background of big data: Taking the user dissimilarity matrix as the recommendation model, we propose IU usercf and UDB slope one recommendation algorithm. The two algorithms based on incremental update recommendation model have good scalability, can effectively deal with big data, and have high prediction accuracy. The proposed algorithm is applied to the actual system, taking e-commerce logistics service as the recommendation object and iu-usercf as the recommendation algorithm, the personalized recommendation system for e-commerce logistics service is constructed. The e-commerce logistics service recommendation system explores the application practice of recommendation algorithm under big data, and enriches the application scenarios of personalized recommendation technology.

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.

Study the Personal Push Service of University Library Based on Big Data Mining

2014 ◽  
Vol 998-999 ◽  
pp. 1261-1265 ◽  
Author(s):  
Cheng Yi ◽  
Ying Xia ◽  
Zhi Yong Zhang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Association Rules ◽  
Recommendation System ◽  
Personalized Recommendation ◽  
Data Resource ◽  
Big Data Mining ◽  
University Library ◽  
Library Resources ◽  
Classical Association

It expounds the big data and the relevant theoretical knowledge of big data mining, In view of the lack of effective analysis of the data resource access in delivery service of university library, this paper designs the personalized recommendation system service model of university library, with clustering analysis and association rules theory as the foundation of technology. And it introduces in detail how to cluster according to the user's attribute characteristics and how to introduce minimum support to opti-mize on the basis of the classical association rules algorithm. Experiments show that the improved algorithm can improves the utilization of library resources.

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.

A Novel Distributed Recommendation Framework Using Big Data in Social Context

2017 ◽  
Vol 31 (08) ◽  
pp. 1759015 ◽  
Author(s):  
Gaochao Xu ◽  
Yan Ding ◽  
Yuqiang Jiang ◽  
Ming Hu ◽  
Jia Zhao
Keyword(s):  
Big Data ◽  
Social Context ◽  
Supervised Learning ◽  
Recommendation System ◽  
Learning Algorithm ◽  
Recommendation Systems ◽  
Computer Hardware ◽  
Personalized Recommendation ◽  
System Framework ◽  
Current Recommendation

Recently big data have become a research hotspot and been successfully exploited in a few applications such as data mining and business modeling. Although big data contain a plenty of treasures for all the fields of computer science, it is very difficult for the current computing paradigms and computer hardware to efficiently process and utilize big data to attain what are looked forward to. In this work, we explore the possibility of employing big data in recommendation systems. We have proposed a simple recommendation system framework BDRSF (Big Data Recommendation System Framework), which is based on big data with social context theories and has abilities in obtaining the Recommender based on the idea of supervised learning through big data training. Its main idea can be divided into three parts: (1) reduce the scale of the current recommendation problems according to the essence of recommending; (2) design a rational Recommender and propose a novel supervised learning algorithm to get it; (3) utilize the Recommender to deal with the later recommendation problems. Experimental results show that BDRSF outperforms conventional recommendation systems, which clearly indicates the effectiveness and efficiency of big data with social context in personalized recommendation.

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