Big Data Mining Using Collaborative Filtering

Author(s):  
Anu Saini

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.

Web Services ◽  
2019 ◽  
pp. 702-711
Author(s):  
Anu Saini

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.


2014 ◽  
Vol 998-999 ◽  
pp. 1261-1265 ◽  
Author(s):  
Cheng Yi ◽  
Ying Xia ◽  
Zhi Yong Zhang

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.


2018 ◽  
Vol 435 ◽  
pp. 124-149 ◽  
Author(s):  
Jianguo Chen ◽  
Kenli Li ◽  
Huigui Rong ◽  
Kashif Bilal ◽  
Nan Yang ◽  
...  

Author(s):  
Lakshmikanth Paleti ◽  
P. Radha Krishna ◽  
J.V.R. Murthy

Recommendation systems provide reliable and relevant recommendations to users and also enable users’ trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user’s friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users’ opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility.


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