Abstract
One of the key foundations of personalized recommendation in a social network is the relationship strength between social network users. The improvement for recommendation accuracy is mostly tied to the precise evaluation of the relationship strengths. With most of the selected factors affecting the relationship strength between users are too simple, the existed researches show low accuracy in calculating the strength, especially those factors related to topic and indirect links. We propose an online social networks users relationship strength estimation model which incorporates topic classification and indirect relationship. We adopt K-means clustering method using ABC algorithm to cluster all the interactive activity documents and calculate the correlation between clusters and activity topic name. After that, we compute the relationship strength between users which belong to the same topic on top of the user profile and interaction data. To accomplish this we employ a language model based on sentiment classification approach and take similarity, timeliness, and interactivity into account. We conduct experiments on two microblog datasets and the results show that the proposed model is promising and can be used to improve the performances of various applications.
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References
Baecchi C, Uricchio T, Bertini M et al (2016) A multimodal feature learning approach for sentiment analysis of social network multimedia[J]. Multimedia Tools and Applications 75(5):2507–2525
Cheng W, Liu B (2015) Empirical study on the personal information disclosure of micro-blog UsersBased on credibility analysis: taking the Sina micro-blog as example. Journal of Intelligence 08:169–176
Chunhua Ju, Chonghuan Xu (2013). A new collaborative recommendation approach based on users clustering using artificial bee colony algorithm. The Scientific World Journal 2013: Article ID 869658
Deng Z-S (2015) An entropy model to infer social strength based on texts of subjects. Jisuanji Yu Xiandaihua 02:30–33
Gilbert E, Karahalios K (2009) Predicting tie strength with social media. In Proceedings of the SIGCHI conference on human factors in computing systems. ACM 211–220
Granovetter M (1973) The strength of weak ties. Am J Sociol 78:1360–1380
Hu Y, Lu R, Li X et al (2007) Research on language modeling based sentiment classification of text. Journal of Computer Research and Development 44(9):1469–1475
Kim M, Park SO (2013) Group affinity based social trust model for an intelligent movie recommender system[J]. Multimedia tools and applications 64(2):505–516
Li P, Yu Y, Li Y et al (2015) Improve LDA microblogs topics model based on weight microblogs chain. Application Research of Computers 07:1–5
Lin X, Shang T, Liu J (2014) An estimation method for relationship strength in weighted social network graphs. Journal of Computer and Communications 2(4):82–89
Liu F, Lee HJ (2010) Use of social network information to enhance collaborative filtering performance. Expert Syst Appl 37(7):4772–4778
Nuñez-Gonzalez JD, Graña M, Apolloni B (2015) Reputation features for trust prediction in social networks. Neurocomputing 166:1–7
Pham H, Shahabi C, Liu Yan (2013) Ebm: an entropy based model to infer social strength from spationtemporal data. In: Proceedings of ACM SIGMOD conference 265–276
Shen H, Yuan Q (2014) A classification study on the strength of social relationship based on social network. Journal of the China Society for Scientific and Technical Information 8(33):846–859
Wilson C, Boe B, Sala A et al. (2009) User interactions in social networks and their implications. In Proceedings of the 4th ACM European conference on computer systems 205–218
Wu F, Huang Y, Song Y (2016) Structured microblog sentiment classification via social context regularization. Neurocomputing 175:599–609
Xiang R, Neville J, Rogati M (2010) Modeling relationship strength in online social networks. In: Proceedings of ACM International Conference on World Wide Web
Xu K, Zou K, Huang Y et al (2016) Mining community and inferring friendship in mobile social networks. Neurocomputing 174:605–616
Zhao Y, Li Y (2012) Research on forecasting personality traits and relationship strength of social network users. In Proceedings of the seventh MAM conference on business intelligence 10
Zhao X, Yuan J, Li G et al (2012) Relationship strength estimation for online social networks with the study on Facebook. Neurocomputing 95:89–97
Zhao W, Zhao Y, Zhu Q et al (2013) A simulation study on information diffusion in social network under Web2.0 environment. Journal of the China Society for Scientific 32(5):511–521
Zhou X, Wang W, Jin Q (2015) Multi-dimensional attributes and measures for dynamical user profiling in social networking environments[J]. Multimedia Tools and Applications 74(14 k):5015–5028
Zhu W (2014) Research on user similarity function of recommender systems. Chongqing: College of Computer Science, Chongqing University, 1–49
Acknowledgments
This research is supported by The National Key Technology R&D Program of China (Grant 2014BAH24F06); Natural Science Foundation of China (No.71571162); Zhejiang Province philosophy social sciences planning project (No.16NDJC188YB); College Students” science and technology innovation activities of Zhejiang Province (2016R408080). This research is supported by the Contemporary Business and Trade Research Center of Zhejiang Gongshang University which is the Key Research Institutes of Social Sciences and Humanities Ministry of Education (14JJD630011). The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation.
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Ju, C., Tao, W. A novel relationship strength model for online social networks. Multimed Tools Appl 76, 17577–17594 (2017). https://doi.org/10.1007/s11042-017-4408-4
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DOI: https://doi.org/10.1007/s11042-017-4408-4