A Systematic Mapping Study of Content Based Filtering Recommender Systems

  • Mahir JainEmail author
  • Suraj SinghEmail author
  • K. ChandrasekaranEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 26)


There has been extremely limited use of recommender systems for clothing suggestions. A clear idea of where recommender systems are used would facilitate the correct method of implementation for the domain given above. In order to propose a solution, there is a need to properly analyse the various existing approaches and solutions developed in a particular field. This study will help us gain clarity to answer several research questions in the chosen domain. A systematic mapping study is carried out to identify as well as classify the research papers pertaining to the chosen field.


Content filtering Recommender systems Systematic mapping study 


  1. 1.
    Peterson, K., Feldt, R., Mujtaba, S., Mattsson, M.: Systematic mapping studies in software engineering. In: Proceedings of the 12th International Conference on Evaluation and Assessment in Software Engineering, pp. 71–80 (2008)Google Scholar
  2. 2.
    Yu-Chu, L., Kawakita, Y., Suzuki, E., Ichikawa, H.: Personalized clothing-recommendation system based on a modified bayesian network. In: 2012 IEEE/IPSJ 12th International Symposium on Applications and the Internet, Izmir, pp. 414–417 (2012)Google Scholar
  3. 3.
    Marques, M.R., Quispe, A., Ochoa, S.F.: A systematic mapping study on practical approaches to teaching software engineering. In: 2014 IEEE Frontiers in Education Conference (FIE) Proceedings, Madrid, pp. 1–8 (2014)Google Scholar
  4. 4.
    Mahmoud, D.S., John, R.I.: Enhanced content-based filtering algorithm using Artificial Bee Colony optimisation. In: 2015 SAI Intelligent Systems Conference (IntelliSys), London, pp. 155–163 (2015).
  5. 5.
    Thotharat, N.: Thai local product recommendation using ontological content based filtering. In: 2017 9th International Conference on Knowledge and Smart Technology (KST), Chonburi, pp. 45–49 (2017).
  6. 6.
    Giordano, D., Kavasidis, I., Pino, C., Spampinato, C.: Content based recommender system by using eye gaze data. In: Spencer, S.N. (ed.) Proceedings of the Symposium on Eye Tracking Research and Applications, (ETRA 2012), pp. 369–372. ACM, New York (2012)Google Scholar
  7. 7.
    Vaidya, N., Khachane, A.R.: Recommender systems-the need of the ecommerce ERA. In: International Conference on Computing Methodologies and Communication (ICCMC), Erode, 2017, pp. 100–104 (2017).
  8. 8.
    Patel, B., Desai, P., Panchal, U.: Methods of recommender system: a review. In: 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, pp. 1–4 (2017)Google Scholar
  9. 9.
    Erkin, Z., Beye, M., Veugen, T., Lagendijk, R.L.: Privacy-preserving content-based recommender system. In: Proceedings of the on Multimedia and security (MM&Sec 2012), pp. 77–84. ACM, New York (2012)Google Scholar
  10. 10.
    Purwitasari, D., Fatichah, C., Purnama, I.K.E., Sumpeno, S., Purnomo, M.H.: Inter-departmental research collaboration recommender system based on content filtering in a cold start problem. In: 2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA), Hiroshima, pp. 177–184 (2017)Google Scholar
  11. 11.
    Guo, W., Gao, X., Xiao, Q.: Bayesian optimization algorithm for learning structure of dynamic Bayesian networks from incomplete data. In: 2008 Chinese Control and Decision Conference, Yantai, Shandong, pp. 2088–2093 (2008)Google Scholar
  12. 12.
    De Pessemier, T., Coppens, S., Geebelen, K., et al.: Multimed. Tools Appl. 58, 167 (2012). Scholar
  13. 13.
    Gu, Y., Zhao, B., Hardtke, D., Sun, Y.: Learning global term weights for content-based recommender systems. In: Proceedings of the 25th International Conference on World Wide Web (WWW 2016). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, pp. 391–400 (2016)Google Scholar
  14. 14.
    Zang, Y., An, Y., Hu, X.T.: Automatically recommending healthy living programs to patients with chronic diseases through hybrid content-based and collaborative filtering. In: 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Belfast, pp. 578–582 (2014)Google Scholar
  15. 15.
    Wen, Z., Zhu, Y., Peng, Z.: Survey on web image content-based filtering technology. In: 2009 First International Conference on Information Science and Engineering, Nanjing, pp. 1463–1466 (2009)Google Scholar
  16. 16.
    Semeraro, G., Lops, P., Basile, P., de Gemmis, M.: Knowledge infusion into content-based recommender systems. In: Proceedings of the Third ACM Conference on Recommender Systems (RecSys 2009), pp. 301–304. ACM, New York (2009)Google Scholar
  17. 17.
    Zitouni, H., Meshoul, S., Taouche, K.: Improving content based recommender systems using linked data cloud and FOAF vocabulary. In: Proceedings of the International Conference on Web Intelligence (WI 2017), pp 988–992. ACM, New York (2017)Google Scholar
  18. 18.
    Uddin, M.N., Shrestha, J., Jo, G.S.: Enhanced content-based filtering using diverse collaborative prediction for movie recommendation. In: 2009 First Asian Conference on Intelligent Information and Database Systems, Dong Hoi, pp. 132–137 (2009)Google Scholar
  19. 19.
    Santos, I., Miñambres-Marcos, I., Galán-García, P., Santamaría-Ibirika, A., Bringas, P.G.: Twitter content-based spam filtering. In: International Joint Conference SOCO’13-CISIS’13-ICEUTE’13. Advances in Intelligent Systems and Computing, vol. 239. Springer (2014)Google Scholar
  20. 20.
    de Gemmis, M., Lops, P., Semeraro, G., Basile, P.: Integrating tags in a semantic content-based recommender. In: Proceedings of the 2008 ACM Conference on Recommender systems (RecSys 2008). ACM, New York (2008). Web-based Applications and Services (iiWAS ’16), ACM, New York, NY, USA, 7–11Google Scholar
  21. 21.
    Ghazanfar, M.A., Prugel-Bennett, A.: A scalable, accurate hybrid recommender system. In: 2010 Third International Conference on Knowledge Discovery and Data Mining, Phuket, pp. 94–98 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology KarnatakaSurathkal, MangaloreIndia

Personalised recommendations