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Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 32

Exploiting Social Capital for Recommendation in Social Networks

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DOI: http://dx.doi.org/10.15439/2022F39

Citation: Communication Papers of the 17th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 32, pages 55–62 ()

Full text

Abstract. Many computational techniques have been proposed by social networks to analyze the users' behaviors to recommend relevant content for them. Social networks generate a huge volume of information, which users cannot consume, generating a problem known as information overload. This way, filtering relevant information to help users with this problem becomes necessary. Social networks have many available features, such as relationships and interactions, which can be used to investigate the users' behaviors regarding news on their feed. The value of news can be defined as Social Capital, which is used by this work to model the user's preferences. This paper aims to investigate, model, and quantify interactions on social networks by exploiting social capital to develop a recommender system. Hence, in order to evaluate recommendations, an experiment was conducted with real users. Results show that our proposal was able to generate relevant recommendations on at least 62\% of the scenarios.

References

  1. K. K. Kapoor, K. Tamilmani, N. P. Rana, P. Patil, Y. K. Dwivedi, and S. Nerur, “Advances in social media research: Past, present and future,” Information Systems Frontiers, vol. 20, no. 3, pp. 531–558, Nov. 2017. http://dx.doi.org/10.1007/s10796-017-9810-y
  2. P. Bourdieu, “The forms of capital,” in Readings in Economic Sociology. Blackwell Publishers Ltd, 2002, pp. 280–291.
  3. J. Nahapiet and S. Ghoshal, “Social capital, intellectual capital, and the organizational advantage,” Academy of Management Review, vol. 23, no. 2, pp. 242–266, Apr. 1998. http://dx.doi.org/10.5465/amr.1998.533225
  4. C. Spratt, J. Hong, K. McAreavey, and W. Liu, “Community-based measures for social capital,” in Studies in Computational Intelligence. Springer International Publishing, Dec. 2018, pp. 327–338.
  5. J. Coleman, Foundations of social theory. Cambridge, Mass: Belknap Press of Harvard University Press, 1990.
  6. G. Pijpers, Information Overload: A System for Better Managing Everyday Data, ser. Microsoft executive leadership series. John Wiley and Sons Ltd, 2010.
  7. K. Bontcheva, G. Gorrell, and B. Wessels, “Social media and information overload: Survey results,” 06 2013.
  8. F. Ricci, L. Rokach, and B. Shapira, Recommender Systems: Introduction and Challenges. Springer US, 2015, ch. 1, pp. 1–34.
  9. S. Forouzandeh, A. Sheikhahmadi, A. R. Aghdam, and S. Xu, “New centrality measure for nodes based on user social status and behavior on facebook,” International Journal of Web Information Systems, vol. 14, no. 2, pp. 158–176, Jun. 2018. http://dx.doi.org/10.1108/ijwis-07-2017-0053
  10. Y. Li, J. Liu, and J. Ren, “Social recommendation model based on user interaction in complex social networks,” PLOS ONE, vol. 14, no. 7, p. e0218957, Jul. 2019. http://dx.doi.org/10.1371/journal.pone.0218957
  11. N. Bartley, A. Abeliuk, E. Ferrara, and K. Lerman, “Auditing algorithmic bias on twitter,” in 13th ACM Web Science Conference 2021. ACM, Jun. 2021. [Online]. Available: https://doi.org/10.1145/3447535.3462491
  12. T. O’Reilly, “What is web 2.0,” Sep 2005. [Online]. Available: https://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html
  13. F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Recommender Systems Handbook, F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, Eds. Springer, 2011.
  14. S. Berkovsky, J. Freyne, and G. Smith, “Personalized network updates: Increasing social interactions and contributions in social networks,” in User Modeling, Adaptation, and Personalization. Springer Berlin Heidelberg, 2012, pp. 1–13.
  15. W. Feng and J. Wang, “Retweet or not?” in Proceedings of the sixth ACM international conference on Web search and data mining - WSDM '13. ACM Press, 2013. http://dx.doi.org/10.1145/2433396.2433470
  16. C. Li and F. Xiong, “Social recommendation with multiple influence from direct user interactions,” IEEE Access, vol. 5, pp. 16 288–16 296, 2017. http://dx.doi.org/10.1109/ACCESS.2017.2739752
  17. D. P. Karidi, Y. Stavrakas, and Y. Vassiliou, “Tweet and followee personalized recommendations based on knowledge graphs,” Journal of Ambient Intelligence and Humanized Computing, vol. 9, no. 6, pp. 2035–2049, Apr. 2017.
  18. H. Bo, R. McConville, J. Hong, and W. Liu, “Social network influence ranking via embedding network interactions for user recommendation,” in Companion Proceedings of the Web Conference 2020. ACM, Apr. 2020. http://dx.doi.org/10.1145/3366424.3383299
  19. F. Durao and P. Dolog, “Improving tag-based recommendation with the collaborative value of wiki pages for knowledge sharing,” Journal of Ambient Intelligence and Humanized Computing, vol. 5, no. 1, pp. 21–38, Apr. 2012. http://dx.doi.org/10.1007/s12652-012-0119-x
  20. F. Abel, Q. Gao, G.-J. Houben, and K. Tao, “Analyzing user modeling on twitter for personalized news recommendations,” in User Modeling, Adaption and Personalization. Springer Berlin Heidelberg, 2011, pp. 1–12.
  21. Y. Bachrach, M. Kosinski, T. Graepel, P. Kohli, and D. Stillwell, “Personality and patterns of facebook usage,” in Proceedings of the 3rd Annual ACM Web Science Conference on - WebSci '12. ACM Press, 2012. http://dx.doi.org/10.1145/2380718.2380722
  22. F. Abel, Q. Gao, G.-J. Houben, and K. Tao, “Twitter-based user modeling for news recommendations,” in Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, ser. IJCAI ’13. AAAI Press, 2013. http://dx.doi.org/10.5555/2540128.2540558 p. 2962–2966.
  23. G. Kazai, I. Yusof, and D. Clarke, “Personalised news and blog recommendations based on user location, facebook and twitter user profiling,” in Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, Jul. 2016. http://dx.doi.org/10.1145/2911451.2911464
  24. R. K. Mudgal, R. Niyogi, A. Milani, and V. Franzoni, “Analysis of tweets to find the basis of popularity based on events semantic similarity,” International Journal of Web Information Systems, vol. 14, no. 4, pp. 438–452, Nov. 2018. http://dx.doi.org/10.1108/ijwis-11-2017-0080
  25. M. E. Timmerman, “Principal component analysis,” Journal of the American Statistical Association, vol. 98, no. 464, pp. 1082–1083, dec 2003. http://dx.doi.org/10.1198/jasa.2003.s308
  26. A. Aleahmad, P. Karisani, M. Rahgozar, and F. Oroumchian, “OLFinder: Finding opinion leaders in online social networks,” Journal of Information Science, vol. 42, no. 5, pp. 659–674, Jul. 2016. doi: 10.1177/0165551515605217
  27. J. Bullas, “What is your twitter reputation?” 2011. [Online]. Available: https://www.jeffbullas.com/what-is-your-twitter-reputation/
  28. T. H. P. Silva, A. H. F. Laender, and P. O. S. V. de Melo, “On knowledge-transfer characterization in dynamic attributed networks,” Social Network Analysis and Mining, vol. 10, no. 1, Jun. 2020.
  29. L. Camacho, J. Faria, S. Alves-Souza, and L. Filgueiras, “Social tracks: Recommender system for multiple individuals using social influence,” in Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0008166503630371
  30. Y. Bae and H. Lee, “Sentiment analysis of twitter audiences: Measuring the positive or negative influence of popular twitterers,” Journal of the American Society for Information Science and Technology, vol. 63, no. 12, pp. 2521–2535, Nov. 2012. http://dx.doi.org/10.1002/asi.22768
  31. F. Isinkaye, Y. Folajimi, and B. Ojokoh, “Recommendation systems: Principles, methods and evaluation,” Egyptian Informatics Journal, vol. 16, no. 3, pp. 261–273, nov 2015. http://dx.doi.org/https://doi.org/10.1016/j.eij.2015.06.005
  32. D. Jannach, M. Zanker, A. Felfernig, and G. Friedrich, Recommender Systems: An Introduction. Cambridge. Cambridge University Press, 2010.
  33. E. Lee, Y. J. Kim, and J. Ahn, “How do people use facebook features to manage social capital?” Computers in Human Behavior, vol. 36, pp. 440–445, Jul. 2014. http://dx.doi.org/10.1016/j.chb.2014.04.007
  34. J. Vinagre, A. M. Jorge, and J. Gama, “Evaluation of recommender systems in streaming environments,” 2014. http://dx.doi.org/10.13140/2.1.4381.5367
  35. R. Zhang, H. Bao, H. Sun, Y. Wang, and X. Liu, “Recommender systems based on ranking performance optimization,” Frontiers of Computer Science, vol. 10, no. 2, pp. 270–280, Jul. 2015. http://dx.doi.org/10.1007/s11704-015-4584-1