Content Curatorship and Collaborative Filtering: A Symbolic Interactionist Approach

  • Kerry Chipp
  • Carola Strandberg
  • Atanu Nath
  • Meyser Abduljabber
Conference paper
Part of the Developments in Marketing Science: Proceedings of the Academy of Marketing Science book series (DMSPAMS)


This paper explores the premise whether sophisticated algorithms that drive curatorship of content for consumers consider a symbolic interactionist perspective on consumer desire for content and whether content offerings, personalisation and the consequent shaping of curatorship algorithms can be based on such an understanding. Curatorship of online content, whether this be product or information based, drives value, consumer engagement and profitability. Curatorship and recommender systems also deliver a personalised experience of the product or services. A review of the reasoning behind such systems reveals that most follow an empirical perspective, namely, the use of statistical tools and information systems algorithms on a behavioural dataset. A theoretically driven approach appears to be lacking. This paper seeks a theoretical approach to online content curatorship embedded in symbolic interactionism. In addition, it seeks to tease out the approach to one that embraces both notions of content curation based on similarity but also on a desire for difference and change. The paper looks at symbolic interactionism in the context of social and individual selves, its role in collaborative filtering, advances a set of propositions for a curation and collaborative filtering model and ends with the possible implications for marketing.


Symbolic interactionism Content curation Collaborative filtering Online curation 


  1. Ariely, D., & Levav, J. (2000). Sequential choice in group settings: Taking the road less traveled and less enjoyed. Journal of Consumer Research, 27(3), 279–290.CrossRefGoogle Scholar
  2. Arnould, E. J., Price, L., & Zinkhan, G. M. (2002). Consumers. New York: McGraw-Hill/Irwin.Google Scholar
  3. Barragáns-Martínez, A. B., Costa-Montenegro, E., Burguillo, J. C., Rey-López, M., Mikic-Fonte, F. A., & Peleteiro, A. (2010). A hybrid content-based and item-based collaborative filtering approach to recommend TV programs enhanced with singular value decomposition. Information Sciences, 180(22), 4290–4311.CrossRefGoogle Scholar
  4. Berger, J., & Heath, C. (2007). Where consumers diverge from others: Identity signaling and product domains. Journal of Consumer Research, 34(2), 121–134.CrossRefGoogle Scholar
  5. Blumer, H. (1969). Symbolic interactionism perspective and method. New Jersey: Prentice Hall Upper Saddle River.Google Scholar
  6. Bobadilla, J., Ortega, F., & Hernando, A. (2012). A collaborative filtering similarity measure based on singularities. Information Processing & Management, 48(2), 204–217.CrossRefGoogle Scholar
  7. Cheng, W., Yin, G., Dong, Y., Dong, H., & Zhang, W. (2016). Collaborative filtering recommendation on users’ interest sequences. PloS One, 11(5), 1–17.Google Scholar
  8. Cova, B., & White, T. (2010). Counter-brand and alter-brand communities: The impact of web 2.0 on tribal marketing approaches. Journal of Marketing Management, 26(3–4), 256–270.CrossRefGoogle Scholar
  9. Davis, J. L. (2016). Curation: A theoretical treatment. Information, Communication & Society, 4462(August), 1–14.Google Scholar
  10. Davis, J. L., & Jurgenson, N. (2014). Context collapse: Theorizing context collusions and collisions. Information, Communication & Society, 17(4), 1–10.CrossRefGoogle Scholar
  11. Fine, G. A. (1993). The sad demise, mysterious disappearance, and glorious triumph of symbolic interactionism. Annual Review of Sociology, 19(1), 61–87.CrossRefGoogle Scholar
  12. Fotopoulou, A., & Couldry, N. (2015). Telling the story of the stories: Online content curation and digital engagement. Information, Communication & Society, 18(2), 235–249.CrossRefGoogle Scholar
  13. Hall, P. M. (1972). A symbolic interactionist analysis of politics. Sociological Inquiry, 42, 35–75.CrossRefGoogle Scholar
  14. Hogan, B. (2010). The presentation of self in the age of social media: Distinguishing performances and exhibitions online. Bulletin of Science, Technology & Society, 30(6), 377–386.CrossRefGoogle Scholar
  15. Hollander, B. A. (2008). Tuning out or tuning elsewhere? Partisanship, polarization, and media migration from 1998 to 2006. Journalism & Mass Communication Quarterly, 85(1), 23–40.CrossRefGoogle Scholar
  16. Huber, J. (1973). Symbolic interaction as a pragmatic perspective: The bias of emergent theory. American Sociological Review, 38, 278–284.Google Scholar
  17. Jeong, B., Lee, J., & Cho, H. (2010). Improving memory-based collaborative filtering via similarity updating and prediction modulation. Information Sciences, 180(5), 602–612.CrossRefGoogle Scholar
  18. Kim, H. S., & Drolet, A. (2003). Choice and self-expression: A cultural analysis of variety-seeking. Journal of Personality and Social Psychology, 85(2), 373–382.CrossRefGoogle Scholar
  19. Kim, H.-N., Ha, I., Lee, K.-S., Jo, G.-S., & El-Saddik, A. (2011). Collaborative user modeling for enhanced content filtering in recommender systems. Decision Support Systems, 51(4), 772–781.CrossRefGoogle Scholar
  20. Koren, Y. (2010). Collaborative filtering with temporal dynamics. Communications of the ACM, 53(4), 87–89.CrossRefGoogle Scholar
  21. Lazer, D. (2015). The rise of the social algorithm. Science, 348(6239), 1090.CrossRefGoogle Scholar
  22. Lee, J., Lee, D., Lee, Y.-C., Hwang, W.-S., & Kim, S.-W. (2016). Improving the accuracy of top-N recommendation using a preference model. Information Sciences, 348, 290–304.CrossRefGoogle Scholar
  23. Leigh, J. H., & Gabel, T. G. (1992). Symbolic interactionism: Its effects on consumer behaviour and implications for marketing strategy. Journal of Services Marketing, 6(3), 5–16.CrossRefGoogle Scholar
  24. Li, Y.-M., Hsiao, H.-W., & Lee, Y.-L. (2013). Recommending social network applications via social filtering mechanisms. Information Sciences, 239, 18–30.CrossRefGoogle Scholar
  25. Liao, C.-L., & Lee, S.-J. (2016). A clustering based approach to improving the efficiency of collaborative filtering recommendation. Electronic Commerce Research & Applications, 18, 1–9.CrossRefGoogle Scholar
  26. Lu, J., Wu, D., Mao, M., Wang, W., & Zhang, G. (2015). Recommender system application developments: A survey. Decision Support Systems, 74, 12–32.CrossRefGoogle Scholar
  27. Maines, D. R. (1977). Social organization and social structure in symbolic interactionist thought. Annual Review of Sociology, 3, 235–259.CrossRefGoogle Scholar
  28. Maines, D. R. (1988). Myth, text, and interaction complicity in the neglect of Blumer's macrosociology. Symbolic Interaction, 11(1), 43–57.CrossRefGoogle Scholar
  29. McPhail, C., & Rexroat, C. (1979). Mead vs. Blumer: The divergent methodological perspectives of social behaviorism and symbolic interactionism. American Sociological Review, 44(3), 449–467.Google Scholar
  30. Mead, G. H. (1934). Mind, self and society (Vol. 111). Chicago: University of Chicago Press.Google Scholar
  31. Nilashi, M., Jannach, D., bin Ibrahim, O., & Ithnin, N. (2015). Clustering- and regression-based multi-criteria collaborative filtering with incremental updates. Information Sciences, 293, 235–250.CrossRefGoogle Scholar
  32. Noble, C. H., & Walker, B. A. (1997). Exploring the relationships among liminal transitions, symbolic consumption, and the extended self. Psychology & Marketing, 14(1), 29–47.CrossRefGoogle Scholar
  33. Ortega, F., Hernando, A., Bobadilla, J., & Kang, J. H. (2016). Recommending items to group of users using matrix factorization based collaborative filtering. Information Sciences, 345, 313–324.CrossRefGoogle Scholar
  34. Ortega, F., Sánchez, J.-L., Bobadilla, J., & Gutiérrez, A. (2013). Improving collaborative filtering-based recommender systems results using Pareto dominance. Information Sciences, 239, 50–61.CrossRefGoogle Scholar
  35. Papyrina, V. (2012). If I want you to like me, should I be like you or unlike you? The effect of prior positive interaction with the group on conformity and distinctiveness in consumer decision making. Journal of Consumer Behaviour, 11(6), 467–476.CrossRefGoogle Scholar
  36. Piacentini, M., & Mailer, G. (2004). Symbolic consumption in teenagers' clothing choices. Journal of Consumer Behaviour, 3(3), 251–262.CrossRefGoogle Scholar
  37. Scheibehenne, B., Greifeneder, R., & Todd, P. M. (2010). Can there ever be too many options? A meta-analytic review of choice overload. Journal of Consumer Research, 37(3), 409–425.CrossRefGoogle Scholar
  38. Snow, D. A. (2001). Extending and broadening Blumer’s conceptualization of symbolic interactionism. Symbolic Interaction, 24(3), 367–377.CrossRefGoogle Scholar
  39. Solomon, M. R. (1983). The role of products as social stimuli: A symbolic interactionism perspective. Journal of Consumer Research, 10(3), 319–329.CrossRefGoogle Scholar
  40. Stryker, S. (1988). Substance and style: An appraisal of the sociological legacy of Herbert Blumer. Symbolic Interaction, 11(1), 33–42.CrossRefGoogle Scholar
  41. Venkatesan, M. (1966). Experimental study of consumer behavior conformity and independence. Journal of Marketing Research (JMR), 3(4), 384–387.CrossRefGoogle Scholar
  42. Warren, C., & Campbell, M. C. (2014). What makes things cool? How autonomy influences perceived coolness. Journal of Consumer Research, 41(August), 543–563.CrossRefGoogle Scholar
  43. Xin, L. U. O., Ouuyang, Y., & Zhang, X. (2011). Improving latent factor model based collaborative filtering via integrated folksonomy factors. International Journal of Uncertainty, Fuzziness & Knowledge-Based Systems, 19(2), 307–327.CrossRefGoogle Scholar

Copyright information

© Academy of Marketing Science 2018

Authors and Affiliations

  • Kerry Chipp
    • 1
    • 2
  • Carola Strandberg
    • 3
  • Atanu Nath
    • 4
  • Meyser Abduljabber
    • 2
  1. 1.University of PretoriaPretoriaSouth Africa
  2. 2.Royal Institute of Technology (KTH)StockholmSweden
  3. 3.Luleå University of TechnologyLuleåSweden
  4. 4.Western Norway University of Applied SciencesSogndalNorway

Personalised recommendations