The week's pick
Reseach Article
Graph based Recommendation for Distributed Systems
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 168 - Number 4 |
| Year of Publication: 2017 |
| Authors: Vivek Pandey, Padma Bonde |
10.5120/ijca2017914376
|
Vivek Pandey, Padma Bonde . Graph based Recommendation for Distributed Systems. International Journal of Computer Applications. 168, 4 ( Jun 2017), 41-43. DOI=10.5120/ijca2017914376
Abstract
A huge amount of information available through electronically, the requirement for effective information retrieval and the implementation of filtering tools have become more necessary for easy retrieval of relevant information. Recommendation Systems (RS) are the software tools and methods providing recommendation for items as well as services to be of need to a user. These systems are providing widespread success in e-commerce applications now-a-days, with the generation of internet. This paper presents a survey of the area of recommendation systems and illustrates the state of the art of the recommendation technique that are generally classified into three categories: Content based Collaborative, Demographic and Hybrid systems. This paper discusses the advantages and disadvantages of the current survey categories as well as the trustworthiness of the recommendation system in a new dimension as searching the evaluator for more suitable recommendations. In the domain of recommendation system, this work can also help to put forward for the use of researcher as an enabling technology.
References
- Yang Xiaoshan; Zhu Ligu; Zhang Qicong and Feng Dongyu,“A Reserch on Big Data Storage Utilization”, 24th International Confrence on Applied Computing and Information Technolog, 2016 Las Vegas, NY, USA, pp.368-372.
- Satya Ranjan Dash, Satchidananda Dehuri2, “Comparative Study of Different Classification Techniques for Post-Operative Patient Dataset”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 1, Issue 5, July 2013.
- B.Sarwar, G.Karypis and J. Konstan, “Item-based collaborative filtering recommendation algorithms” , Proceedings of the 10th international conference on World Wide Web,pp.285-295.
- Fatos Xhafa , Adriana Bogza and Santi Caballé, “Performance Evaluationof Mahout Clustering Algori-them Using a Twitter Streaming Dataset “IEEE 31st International Conference on Advanced Information Networking and Applications (AINA), 27-29 March 2017,Taiwan
- Kunhui Lin, Jingjin Wang, Meihong Wang, “A Hybrid Recommendation Algorithm Based on Hadoop”, the 9th International Conference on Computer Science Education (ICCSE 2014) 22-24 August , Vancouver, Canada.
- Haipeng You, Hui Li, Yunmin Wang, and Qingzhuang Zhao, “An Improved Collaborative Filtering Recommendation Algorithm Combining Item Clustering and Slope One Scheme”, Proceedings of the International MultiConference of Engineers and Computer Scientists Vol 1,pp 313-316, 2015 Honkong.
- Dalia Sulieman, Maria Malek and Dominique Laurent, “Graph Searching Algorithms For Semantic-Social Recommendation”, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2012
- Yongchang Wang and Ligu Zhu , “Reserch on Collaborative Filtering Recomndantion Algorithem Based On Mahout”, Proceedings of The 4th Intl. conf.on Applied computing and Information Technology, 2016.
- Dr. Senthil Kumar Thangavel, Neetha Susan Thampi, Johnpaul C I, ” Performance Analysis of Various Recommendation Algorithms Using Apache Hadoop and Mahout”, International Journal of Scientific & Engineering Research, Volume 4, Issue 12, December-2013
- G.R.Bamnote and Agrawal,”Evaluting and Implementing collaborative Filtering Sysystems Using Appache Mahout”,International Conference on Computing Communication Control and Automation, 2015.
- Hongqing Guo,Li Chen A personalized recommendation technology for E-commerce website” Proceedings of the 2016 International Conference on Machine Learning and Cybernetics, 10-13 July, 2016,Jeju, South Korea,
- M.Bell, and Y.Koren , “Scalable collaborative filtering with jointly derived neighborhood interpolation weights” ,Data Mining, Seventh IEEE International Conference , Aug 2015pp.43-52.
- Mamadou Diaby, Emmanuel Viennet and Tristan Launay “Toward the next generation of recruitment tools: An oonline social network-based job recommender system” Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on 25-28 Aug. 2013 Niagara Falls, Canada.
- Jingjin Wang, Kunhui Lin,and Jia Li, “An Collaborative Filtering Recommendation Algorithm Based On User Clustering and Slope One Scheme”, Proceedings of The 8th International Conference on Computer Science Education(ICCSE) Colombo Srilanka.
Index Terms
Keywords