Paper Info
Title
A Novel Distributed Recommendation Framework Using Big Data In Social Context
Abstract
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.
Year
DOI
Venue
2017
10.1142/S0218001417590157
INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE
Keywords
Field
DocType
Big data, cloud computing, personalized recommendation, social context
Data science,Social environment,World Wide Web,Computer science,Business model,Artificial intelligence,Big data,Hotspot (Wi-Fi),Machine learning,Cloud computing
Journal
Volume
Issue
ISSN
31
8
0218-0014
Citations 
PageRank 
References 
0
0.34
8
Authors
5
Name
Order
Citations
PageRank
Gaochao Xu118324.11
Yan Ding24012.03
Yuqiang Jiang300.34
Ming Hu420.70
Jia Zhao5404.84