The Personalization Technique for Social Recommender Systems using Machine Learning

Huan Du, Haiyan Chen

2015

Abstract

Recent years have seen the explosive growth of information in the form of web services. Recommender systems suggest items of interest to users based on users’ explicit and implicit feedback and also based on the preferences and interests of other similar users/items. As a small step towards extending the footprint of the applications of big data, this paper tries to depict the machine learning techniques to perform Social network analytics that may provide a 360 degree insight into the social network data. The term machine learning aptly denotes that, the system is made to learn by providing necessary inputs and carefully examining the obtained outputs. The applications of machine learning are as diverse as the applications of big data. Adaptive websites, Bio informatics, Computational advertising, Information retrieval, credit card fraud detection, medical diagnosis, Natural language processing, stock market analysis are some areas where machine learning has found its use.

References

  1. Lloyd Allison, “Types and classes of machine learning and data mining”, Proceedings of the 26th Australasian Computer Science Conference, Vol.16, Page:207-215, 2003.
  2. Luo, X., Xu, Z., Yu, J., and Chen, X. 2011. Building Association Link Network for Semantic Link on Web Resources. IEEE transactions on automation science and engineering, 8(3):482-494.
  3. Xu, Z. et al. 2015. Knowle: a Semantic Link Network based System for Organizing Large Scale Online News Events. Future Generation Computer Systems, 43-44:40-50.
  4. Guojin Liu, Ming zhang, Fei Yan, “ Large Scale social network analysis based on MapReduce”, International conference on computational aspects of social networks, Page:487-490, 2010
  5. Balabanovic,M., Y.Shoham (1997) Fab: Content-based, Collaborative Recommendation, Communications of the ACM, vol. 40, pp. 66-72.
  6. Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A. (2005) Incorporating Contextual Information In Recommender Systems Using A Multidimensional Approach, ACM Transactions on Information Systems, vol. 23, pp.103-145.
  7. Robin Burke (2007) Hybrid Web Recommender Systems, In: The Adaptive Web-Lecture notes in Computer Science, Springer Verlag, pp. 377-408.
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Paper Citation


in Harvard Style

Du H. and Chen H. (2015). The Personalization Technique for Social Recommender Systems using Machine Learning . In Proceedings of the Information Science and Management Engineering III - Volume 1: ISME, ISBN 978-989-758-163-2, pages 138-141. DOI: 10.5220/0006020601380141


in Bibtex Style

@conference{isme15,
author={Huan Du and Haiyan Chen},
title={The Personalization Technique for Social Recommender Systems using Machine Learning},
booktitle={Proceedings of the Information Science and Management Engineering III - Volume 1: ISME,},
year={2015},
pages={138-141},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006020601380141},
isbn={978-989-758-163-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Information Science and Management Engineering III - Volume 1: ISME,
TI - The Personalization Technique for Social Recommender Systems using Machine Learning
SN - 978-989-758-163-2
AU - Du H.
AU - Chen H.
PY - 2015
SP - 138
EP - 141
DO - 10.5220/0006020601380141