Utilizing Social Media Analytics to Recommend Personalized Gifts Using Content-Based and Multicriteria Collaborative Filtering

  • Marisa M. Buctuanon
  • Joana Claire Alegado
  • Jessah Daculan
  • Lauren Christy Ponce
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 886)


The study aims to use Facebook data to create a user profile to be able to recommend personalized gifts and help users to choose the right gift for a certain occasion. The Facebook data includes posts, comments, liked pages and user’s biography. The data gathered are then preprocessed to create a user profile and item profile. The preprocessing stage includes data cleaning, and POS tagging. These profiles can be classified as book lover, fashion fiend, outdoor enthusiast, foodie, music lover and sports fan. These profiles are then mapped through content-based and multicriteria collaborative filtering. In content-based filtering three criteria are used, namely, receiver’s personality, cosine similarity and user’s chosen event. The events include birthday, valentines, wedding, anniversary, father’s day, mother’s day and graduation. Multicriteria collaborative filtering uses Pearson Similarity to distinguish similar users who would likely like the same product. Combining these results, a hybrid system is produced and a desirable list of items is recommended.


Social media analytics Personalized gift recommender system Multicriteria collaborative filtering Content-based filtering Hybrid recommender system 


  1. 1.
    Goodwin, C., Smith, K.L., Spiggle, S.: Gift giving: consumer motivation and the gift purchase process. In: Advances in Consumer Research, pp. 690–698 (1990)Google Scholar
  2. 2.
    Chakrabarti, R., Berthon, P.: Gift giving and social emotions: experience as content. J. Public Aff. 12(2), 154–161 (2012)CrossRefGoogle Scholar
  3. 3.
    Bellogín, A., Cantador, I., Díez, F., Castells, P., Chavarriaga, E.: An empirical comparison of social, collaborative filtering, and hybrid recommenders. ACM Trans. Intell. Syst. Technol. 4(1), 14 (2013)CrossRefGoogle Scholar
  4. 4.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the 10th International Conference on World Wide Web, Hong Kong, pp. 158–167 (2001)Google Scholar
  5. 5.
    Schafer, B., Frankowski, D., Herlocker, J., Shilad, S.: Collaborative filtering recommender systems. In: The adaptive web, pp. 291–324. Springer, Heidelberg (2007)Google Scholar
  6. 6.
    Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative. In: Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, pp. 43–52 (1998)Google Scholar
  7. 7.
    Herlocker, J., Konstan, J., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 230–237 (1999)Google Scholar
  8. 8.
    Hegde, A., Shetty, S.K.: Collaborative filtering recommender system. IJETST 2(7), 2885–2889 (2015)Google Scholar
  9. 9.
    Cano, E., Morisio, M.: Hybrid recommender systems: a systematic literature review. Intell. Data Anal. 21(6), 1487–1524 (2017)CrossRefGoogle Scholar
  10. 10.
    Tomar, P., Arora, P., Goel, A., Saini, D.: Social profile based gift recommendation system. Int. J. Comput. Sci. Inf. Technol. 5(3), 3670–3673 (2014)Google Scholar
  11. 11.
    Yu, Y., Wang, Y.: Design and implementation of a content-based gift recommender system. In: Soft Computing in Information Communication Technology (2014)Google Scholar
  12. 12.
    Zeng, D., Chen, H., Lusch, R., Li, S.-H.: Social media analytics. IEEE Comput. Soc. 13–16 (2010)Google Scholar
  13. 13.
    Boolean Retrieval: Cambridge University Press, Cambridge, 1 April 2009Google Scholar
  14. 14.
    van Meteren, R., van Someren, M.: Using content-based filtering for recommendation. In: Machine Learning in the New Information Age: MLnet/ECML2000 Workshop, pp. 47–56 (2000)Google Scholar
  15. 15.
    Belkin, N.J., Croft, W.B.: Information filtering and information retrieval: Two sides of the same coin? Commun. ACM 35(12), 29–38 (1992)CrossRefGoogle Scholar
  16. 16.
    Toutanova, K., Klein, D., Manning, C.D., Singer, Y.: Feature-rich part-of-speech tagging with a cyclic dependency network. In: SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000), pp. 173–180 (2000)Google Scholar
  17. 17.
    Ramos, J.: Using TF-IDF to determine word relevance in document queries. In: Proceedings of the First Instructional Conference on Machine Learning, vol. 242, pp. 133–142 (2003)Google Scholar
  18. 18.
    Wang, J., de Vries, A.P., Reinders, M.J.: Unifying user-based and item-based collaborative. In: Proceedings of the 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Washington, USA, pp. 501–508 (2006)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Marisa M. Buctuanon
    • 1
  • Joana Claire Alegado
    • 1
  • Jessah Daculan
    • 1
  • Lauren Christy Ponce
    • 1
  1. 1.University of San Jose – RecoletosCebu CityPhilippines

Personalised recommendations