article

Interest prediction in social networks based on Markov chain modeling on clustered users

Authors:

Xianghan Zheng,

Wenzhong GuoAuthors Info & Claims

Concurrency and Computation: Practice & Experience, Volume 28, Issue 14

Pages 3895 - 3909

https://doi.org/10.1002/cpe.3572

Published: 25 September 2016 Publication History

Abstract

Effective user interest prediction is significant for service providers in a set of application scenarios such as user behavior analysis and resource recommendation. However, existing approaches are either incomplete or proprietary. In this paper, user interest prediction based on the Markov chain modeling on clustered users is proposed with the following procedure: collect dataset from 4613 users and more than 16 million messages from Sina Weibo; obtain each user's interest eigenvalue sequence and establish single-Markov chain model; and implement user clustering algorithm for the multi-Markov chain construction in order to divide users into a set of predefined interest categories. The proposed solution is capable of predicting both long-term and short-term user interests based on a suitable selection of the initial state distribution, λ. The proposed solution also proves that short-term interests are consistent with long-term interests if the influences of social or user-related events that cause interruptions e.g., earthquake and birthday are not considered. Furthermore, experiments show that the proposed solution is feasible and efficient and can achieve a higher accuracy of prediction than that of the other approaches such as Support Vector Machine SVM and K-means. Copyright © 2015 John Wiley & Sons, Ltd.

References

[1]

Wasserman S. Social Network Analysis: Methods and Applications. Cambridge University Press, 1994.

[2]

Statista, in: "http://www.statista.com"/ accessed June 2014.

[3]

Chen KH, Han PP, Wu J. User clustering based social network recommendation. Jisuanji Xuebao Chinese Journal of Computers 2013; Volume 36 Issue 2: pp.349-359.

[4]

Agrawal D, Budak C, El Abbadi A, et al. Big data in online social networks: user interaction analysis to model user behavior in social networks//databases in networked information systems. Springer International Publishing, 2014: pp.1-16.

[5]

Xu Y, Guo X, Hao J, et al.Combining social network and semantic concept analysis for personalized academic researcher recommendation. Decision Support Systems 2012; Volume 54 Issue 1: pp.564-573.

[6]

Bao H, Li Q, Liao SS, et al.A new temporal and social PMF-based method to predict users' interests in micro-blogging. Decision Support Systems 2013; Volume 55 Issue 3: pp.698-709.

[7]

Chen HY, Pu J. To adapt to changes in the user long-term and recent interest of personalized recommendation model. Advanced Materials Research 2013; Volume 791: pp.2143-2146.

[8]

Yang SH, Long B, Smola A, et al.Like like alike: joint friendship and interest propagation in social networks{C}//Proceedings of the 20th International Conference on World Wide Web. ACM 2011: pp.537-546.

[9]

Van Iddekinge CH, Putka DJ, Campbell JP. Reconsidering vocational interests for personnel selection: the validity of an interest-based selection test in relation to job knowledge, job performance, and continuance intentions{J}. Journal of Applied Psychology 2011; Volume 96 Issue 1: pp.13.

[10]

La Greca AM, Harrison HM. Adolescent peer relations, friendships, and romantic relationships: do they predict social anxiety and depression? Journal of Clinical Child and Adolescent Psychology 2005; Volume 34 Issue 1: pp.49-61.

[11]

Facebook, in: "http://www.facebook.com"/.

[12]

Welcome to Twitter, in: "http://twitter.com"/.

[13]

Weibo - SINA, in: "http://english.sina.com/weibo/".

[14]

Abraham R, Delmas JF. A continuum-tree-valued Markov process{J}. The Annals of Probability 2012; Volume 40 Issue 3: pp.1167-1211.

[15]

Attenberg J, Pandey S, Suel T. Modeling and predicting user behavior in sponsored search{C}//Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM 2009: pp.1067-1076.

[16]

Zhang Z, Zhou T, Zhang Y. Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs{J}. Physica A: Statistical Mechanics and its Applications 2010; Volume 389: pp.179-186.

[17]

Jung SY, Hong JH, Kim TS. A statistical model for user preference{J}. IEEE Transactions on Knowledge and Data Engineering 2005; Volume 17 Issue 6: pp.834-843.

[18]

Xu Z, Lu R, Xiang L, et al. Discovering user interest on Twitter with a modified author-topic model{C}//Web Intelligence and Intelligent Agent Technology WI-IAT, 2011 IEEE/WIC/ACM International Conference. IEEE, 2011, Volume 1: pp.422-429.

[19]

Zhang ZK, Zhou T, Zhang YC. Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs{J}. Physica A: Statistical Mechanics and its Applications 2010; Volume 389 Issue 1: pp.179-186.

[20]

Banerjee N, Chakraborty D, Dasgupta K, et al.User interests in social media sites: an exploration with micro-blogs{C}//Proceedings of the 18th ACM Conference on Information and Knowledge Management. ACM 2009: pp.1823-1826.

[21]

Xiang L, Yuan Q, Zhao S, et al. Temporal recommendation on graphs via long-and short-term preference fusion {C}//Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2010: pp.723-732.

[22]

Aljandal W, Bahirwani V, Caragea D, et al. Ontology-aware classification and association rule mining for interest and link prediction in social networks {C}//AAAI Spring Symposium: Social Semantic Web: Where Web 2.0 Meets Web 3.0. 2009: 3-8.

[23]

Yan Q, Yi L, Wu L. Human dynamic model co-driven by interest and social identity in the MicroBlog community. Physica A: Statistical Mechanics and its Applications 2012; Volume 391 Issue 4: pp.1540-1545.

[24]

Nori N, Bollegala D, Ishizuka M. Interest prediction on multinomial, time-evolving social graph{C}//IJCAI. 2011, 11: 2507-2512.

[25]

Aggarwal CC, Zhai CX. Mining text data{M}. Springer, 2012.

[26]

Phan XH, Nguyen CT, Le DT, et al.A hidden topic-based framework toward building applications with short web documents{J}. IEEE Transactions on Knowledge and Data Engineering 2011; Volume 23 Issue 7: pp.961-976.

Digital Library

[27]

Wang C, Jin C. Based on the established vocabulary of Yi automatic segmentation system design and implementation. Science Technology and Engineering 2012; Volume 10: pp.020.

[28]

Carpineto C, Romano G. A survey of automatic query expansion in information retrieval{J}. ACM Computing Surveys CSUR 2012; Volume 44 Issue 1: pp.1.

[29]

Teevan J, Ramage D, Morris MR. # TwitterSearch: a comparison of microblog search and web search{C}//Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011: pp.35-44.

[30]

Liu C, Stochastic process fourth edition{M}, Huazhong University of Science and Technology Press, Wuchang Yu Jiashan, 2008 .Volume 8, pp.1-113.

[31]

Oommen BJ, Yazidi A, Granmo OC. An Adaptive Approach to Learning the Preferences of Users in a Social Network Using Weak Estimators. 2012.

[32]

Zhang Z, Zhou T, Zhang Y. Personalized recommendation via integrated diffusion on user-item-tag tripartite graphs. Physica A: Statistical Mechanics and its Applications 2010; Volume 389: pp.179-186.

[33]

Heckerman D, Geiger D, Chickering DM. Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning 1995; Volume 20 Issue 3: pp.197-243.

[34]

Ghaseminezhad MH, Karami A. A novel self-organizing map SOM neural network for discrete groups of data clustering. Applied Soft Computing 2011; Volume 11 Issue 4: pp.3771-3778.

[35]

Hu X, Tang J, Zhang Y, et al. Social spammer detection in microblogging //Proceedings of the Twenty-third international joint conference on Artificial Intelligence. AAAI Press, 2013: pp.2633-2639.

Cited By

Yang HShen JZhou TJi SVijayakumar P(2022)A Flexible and Privacy-Preserving Collaborative Filtering Scheme in Cloud Computing for VANETsACM Transactions on Internet Technology10.1145/342570822:2(1-19)Online publication date: 31-May-2022
https://dl.acm.org/doi/10.1145/3425708
Lu YLi JYang ZOu XXie W(2021)Data mining and social networks processing method based on support vector machine and k-nearest neighborJournal of Computational Methods in Sciences and Engineering10.3233/JCM-20461321:2(435-447)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JCM-204613
Liu JXu J(2021)An Interest Drift Model Based on Markov Chain and its Application in Micro-Blog Recommendation2021 4th International Conference on Information Systems and Computer Aided Education10.1145/3482632.3484057(1865-1870)Online publication date: 24-Sep-2021
https://dl.acm.org/doi/10.1145/3482632.3484057

Recommendations

Modeling Users’ Multifaceted Interest Correlation for Social Recommendation
Advances in Knowledge Discovery and Data Mining
Abstract
Recommender systems suggest to users the items that are potentially of their interests, by mining users’ feedback data on items. Social relations provide an independent source of information about users and can be exploited for improving ...
An effective recommendation method for cold start new users using trust and distrust networks

Recommendation systems analyze the purchasing behavior (e.g., item ratings) of users to learn about their preferences and recommend products or services that may be of interest to them. However, as new users require time to become familiar with ...
A Novel Social Event Recommendation Method Based on Social and Collaborative Friendships
SocInfo 2013: Proceedings of the 5th International Conference on Social Informatics - Volume 8238

Many social network sites (SNSs) provide social event functions to facilitate user interactions. However, it is difficult for users to find interesting events among the huge number posted on such sites. In this paper, we investigate the problem and ...

Comments

Information & Contributors

Information

Published In

cover image Concurrency and Computation: Practice & Experience

Concurrency and Computation: Practice & Experience Volume 28, Issue 14

September 2016

235 pages

ISSN:1532-0626

Issue’s Table of Contents

Publisher

John Wiley and Sons Ltd.

United Kingdom

Publication History

Published: 25 September 2016

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 30 Aug 2024

Other Metrics

View Author Metrics

Citations

Cited By

Yang HShen JZhou TJi SVijayakumar P(2022)A Flexible and Privacy-Preserving Collaborative Filtering Scheme in Cloud Computing for VANETsACM Transactions on Internet Technology10.1145/342570822:2(1-19)Online publication date: 31-May-2022
https://dl.acm.org/doi/10.1145/3425708
Lu YLi JYang ZOu XXie W(2021)Data mining and social networks processing method based on support vector machine and k-nearest neighborJournal of Computational Methods in Sciences and Engineering10.3233/JCM-20461321:2(435-447)Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.3233/JCM-204613
Liu JXu J(2021)An Interest Drift Model Based on Markov Chain and its Application in Micro-Blog Recommendation2021 4th International Conference on Information Systems and Computer Aided Education10.1145/3482632.3484057(1865-1870)Online publication date: 24-Sep-2021
https://dl.acm.org/doi/10.1145/3482632.3484057

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents