Survey and Evolution Study Focusing Comparative Analysis and Future Research Direction in the Field of Recommendation System Specific to Collaborative Filtering Approach

  • Axita Patel
  • Amit Thakkar
  • Nirav Bhatt
  • Purvi Prajapati
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 106)


Recommendation system is a sub-ordinate of information filtrate system that provides users with suggestions for items a user may want. It plays a censorious role in wide range of online shopping, e-commercial services, and social networking applications. In recent years recommendations have changed different ways of communication between users and websites. Recommendation system sorts huge amount of data to determine interest of users and makes search easier. For that purpose many methods have been used. This paper covers different approaches which are used in recommendation system which are: collaborative approach, content-based approach, and hybrid recommendation approach. We have also mentioned several issues that come across recommendation systems.


Recommendation system Collaborative filtering Content-based approach Hybrid approach 


  1. 1.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  2. 2.
    Nagarnaik, P., Thomas, A.: Survey on recommendation system methods. In: 2015 2nd International Conference on Electronics and Communication Systems (ICECS), pp. 1496–1501. IEEE (2015)Google Scholar
  3. 3.
    Shah, L., Gaudani, H., Balani, P.: Survey on recommendation system. System 137(7) (2016)Google Scholar
  4. 4.
    Loskovec, J., Rajaraman, A., Ullman, J.D.: Mining of Massive Datasets (Sound Recording) (2016)Google Scholar
  5. 5.
    Khari, M., Kumar, P.: Evolutionary computation-based techniques over multiple data sets: an empirical assessment. Arab. J. Sci. Eng. 1–11 (2017)Google Scholar
  6. 6.
    Jannach, D., Zanker, M., Felfernig, A., Friedrich, G.: Recommender Systems: An Introduction. Cambridge University Press (2010)Google Scholar
  7. 7.
    Gallege, P., Sandakith, L.: Trust-based service selection and recommendation for online software marketplaces. Ph.D. Dissertation, Purdue University (2016)Google Scholar
  8. 8.
    Ha, T., Lee, S.: Item-network-based collaborative filtering: a personalized recommendation method based on a user’s item network. Inf. Process. Manage. 53(5), 1171–1184 (2017)CrossRefGoogle Scholar
  9. 9.
    Polatidis, N., Georgiadis, C.K.: A multi-level collaborative filtering method that improves recommendations. Expert Syst. Appl. 48 (2016)Google Scholar
  10. 10.
    Haviv, A.: Recommendation Systems in eBay: One of the Largest Semi-Unstructured Marketplace. Newell-simon, 30 NovGoogle Scholar
  11. 11.
    Johnson, R.: Advanced recommendations with collaborative filtering. Notre DameGoogle Scholar
  12. 12.
    Wang, H., Wang, N., Yeung, D.-Y.: Collaborative deep learning for recommender systems. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1235–1244. ACM (2015)Google Scholar
  13. 13.
    Lardinois, F.: Four approaches to music recommendations: Pandora, Mufin, Lala, and eMusic. Read Write Web, Retrieved 12(19), 11 (2009)Google Scholar
  14. 14.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Recommender Systems Handbook, pp. 1–35. Springer, US (2011)Google Scholar
  15. 15.
    Obadić, I., Madjarov, G., Dimitrovski, I., Gjorgjevikj, D.: Addressing item-cold start problem in recommendation systems using model based approach and deep learning. In: International Conference on ICT Innovations, pp. 176–185. Springer, Cham (2017)Google Scholar
  16. 16.
    Ullman, L.R.: Content Based Recommendations (Sound Recording). Stanford University (2016)Google Scholar
  17. 17.
    Ebrahim, Y.: “Memory-Based vs. Model-Based Recommendation System,” Memory-Based vs. Model-Based Recommendation System, 13 October 2012Google Scholar
  18. 18.
    Ullman, L.R.: Implementing Collaborative Filtering (Sound Recording). Stanford University (2016)Google Scholar
  19. 19.
    Roberts, N.: Some of the Challenges of Collaborative Filtering. Quora (2016)Google Scholar
  20. 20.
    Cacheda, F., Carneiro, V., Fernández, D., Formoso, V.: Comparison of collaborative filtering algorithms: limitations of current techniques and proposals for scalable, high-performance recommender systems. ACM Trans. Web (TWEB) 5(1), 2 (2011)Google Scholar
  21. 21.
    Almazro, D., Shahatah, G., Albdulkarim, L., Kherees, M., Martinez, R., Nzoukou, W.: A survey paper on recommender systems. arXiv:1006.5278 (2010)
  22. 22.
    Techlabs, M.: How do recommendation engines work. And what are the benefits? (2017)Google Scholar
  23. 23.
    Krasnoshchok, O.: Collaborative filtering recommender systems—benefits and disadvantages. In: KES Conference (2014)Google Scholar
  24. 24.
    Tuan, D.: Recommender systems-how they work and their impacts, May 2012Google Scholar
  25. 25.
    Kohar, M., Rana, C.: Survey paper on recommendation system. Int. J. Comput. Sci. Inf. Technol. 3(2), 3460–3462 (2012)Google Scholar
  26. 26.
    Bai, B., Fan, Y., Tan, Wei., Zhang, J.: DLTSR: a deep learning framework for recommendation of long-tail web services. IEEE Trans. Services Comput. (2017)Google Scholar
  27. 27.
    Macmanus, R.: 5 Problems of Recommender Systems. Readwrite (2009)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Axita Patel
    • 1
  • Amit Thakkar
    • 1
  • Nirav Bhatt
    • 1
  • Purvi Prajapati
    • 1
  1. 1.Department of Information TechnologyCharotar University of Science and TechnologyAnandIndia

Personalised recommendations