A Novel User Ranking Algorithm In Social Networking Sites For Recommendation

References

[1] Wenting Liu, Guangxia Li, James Cheng. Fast pagerank approximation by adaptive sampling. Knowledge and Information Systems, 42(1):127- 146, 2015.

[2] Rada Mihalcea. Graph-based ranking algorithms for sentence extraction, applied to text summarization. In Proceedings of the ACL 2004 Interactive poster and demonstration sessions, . Association for Computational Linguistics, 2004.

[3] Amin Omidvar, Mehdi Garakani, Hamid R Safarpour. Context based user ranking in forums for expert finding using word dictionary and social network analysis. Information Technology.

[4] Tore Opsahl, Filip Agneessens, John Skvoretz. Node centrality weighted networks: Generalizing degree and shortest paths. Social networks, 32(3):245-251, 2010.

[5] Daniel M Romero, Wojciech Galuba, Sitaram Asur, Bernardo A Huberman. Influence and passivity in social media. In Machine learning and knowledge discovery databases, pages 18-33. Springer, 2011.

[6] Yuanfeng Song, Wilfred Ng, Kenneth Wai- Ting Leung, Qiong Fang. Sfp -rank: significant frequent pattern analysis for effective ranking. Knowledge Information Systems, 43(3):529- 553, 2015.

[7] Yuto Yamaguchi, Tsubasa Takahashi, Toshiyuki Amagasa, Hiroyuki Kitagawa. Turank: Twitter user ranking based on user- tweet graph analysis. Web Information Systems Engineering-WISE 2010, pages 240-253. Springer, 2010.

[8] Jian Jiao, Jun Yan, Haibei Zhao, Weiguo Fan. Expertrank: An expert user ranking algorithm in online communities. In New Trends Information and Service Science, 2009. NISS’09. International Conference on, pages 674-679. IEEE, 2009.

[9] Jon M Kleinberg. Authoritative sources in hyperlinked environment. Journal of the ACM (JACM), 46(5):64-632, 1999.

[10] Jianshu Weng, Ee-Peng Lim, Jing Jiang, Qi He. Twitterrank: finding topic-sensitive influential twitterers. Proceedings of the third ACM international conference Web search and data mining, pages 261- 270. ACM, 2010.

[11] Eytan Bakshy, Jake M Hofman, Winter A Mason, Duncan J Watts. Everyone’s an influencer: quantifying influence on twitter. In Proceedings of fourth ACM international conference on Web search and data mining, pages 65-74. ACM, 2011.

[12] Sergey Brin, Lawrence Page. Reprint The anatomy of a large scale hyper textual web search engine. Computer networks, 56(18):3825- 3833, 2012.

[13] Robert Goodell Brown. Smoothing, forecasting and prediction of discrete series. Courier Corporation, 2004.

[14] Christopher S Campbell, Paul P Maglio, Alex Cozzi, Byron Dom. Expertise identification using email communications. In Proceedings of twelfth international conference on Information knowledge management, pages 528-531. ACM, 2003.

[15] Meeyoung Cha, Hamed Haddadi, Fabricio Benevenuto, P Krishna Gummadi. Measuring user influence in twitter The million follower fallacy. ICWSM, 10(10-17):30, 2010.

[16] Pedro Domingos, Matt Richardson. Mining the network value of customers. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery data mining, pages 57-6 ACM, 2001.

@article{1700604,
      author = {Bhavanam Hanimi Reddy, K.Vikas},
      title = {A Novel User Ranking Algorithm In Social Networking Sites For Recommendation},
      journal = {Iconic Research And Engineering Journals},
      year = {2018},
      volume = {1},
      number = {10},
      pages = {109-114},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1700604.pdf},
      abstract = {The present years have seen an exceptional impact of social networks, for instance, Twitter, which boasts in excess of 200 million customers. In such colossal social stages, the convincing customers are ideal concentrations for viral elevating to potentially contact a gathering of individuals of maximal size. Most proposed algorithms rely upon the linkage structure of the different essential framework to choose the information stream and consequently demonstrate a customer?s affect. From social association perspective, we created a model in light of the dynamic customer collaboration's persistently happening over these linkage structures. In particular, in the Twitter setting we assembled a rule of balanced re tweet communication, and a while later arranged it to disclose the estimations of Twitter customers. Our examinations on honest to goodness Twitter data demonstrated that our proposed show presents one of a kind yet comparably clever situating results. Furthermore, the coordinated conjecture test exhibited the rightness of our model.},
      keywords = {Vitality Ranking, Social Networks, Page rank, Twitter.},
      month = {April},
  }