Machine Learning Based Recommendation System: A Review


Shreya Sharda
Gurpreet Singh Josan


The digital era has created an extreme choice paradigm with an explosion of endless content. A user who is just starting on the platform or looking for a creature can be lost in this ocean. Therefore, it is necessary to design a system that can guide users as per their interest. To overcome this problem, the Recommendation System (RS) came into existence. RS is a tool used to recommend items as per users interests. The benefits of the RS cannot be exaggerated, given the potential impact to improve many of the problems associated with widespread use and over-selection in many web applications. In recent years, Machine learning (ML) shows great interest in different research areas, such as computer vision and Natural Language Processing (NLP), not only because of its stellar performance but also because of its attractive feature of demonstrating learning from scratch. The effect of ML techniques can be seen while applying these techniques to the prediction and recommender system. This paper presented a comprehensive survey on recommendation techniques used in conjunction with the ML approach in many domains. This work aims to find the shortcoming of available RS for different fields and the areas that require more effort to attain higher accuracy


How to Cite
Shreya Sharda, & Gurpreet Singh Josan. (2021). Machine Learning Based Recommendation System: A Review. International Journal of Next-Generation Computing, 12(2), 134–144.


  1. AAMIR, M. and BHUSRY, F. E. 2005. Recommendation system: state of the art approach. In International Journal of Computer Applications, 120.
  2. ADENIYI, D.A.WEI, Z. and YONGQUAN, Y. 2016. Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method. Applied Computing and Informatics 12, 90-108. ADNAN, M.N.M. CHOWDURY, M.R. TAZ, I. AHMED, T. and RAHMAN, R.M. 2014. Content based news
  3. recommendation system based on fuzzy logic. In 2014 International Conference on Informatics, Electronics & Vision (ICIEV), 1-6. IEEE.
  4. BASKOTA, A. and NG, Y.K. 2018. A Graduate School Recommendation System Using the Multi-Class Support Vector Machine and KNN Approaches. In In 2018 IEEE International Conference on Information Reuse and Integration (IRI), 277-284. IEEE.
  5. BEEL, J. GIPP, B. LANGER, S. and BREITINGER, C. 2016. paper recommender systems: a literature survey. In International Journal on Digital Libraries 17, 305-338.
  6. BERTENS, P. GUITART, A. CHEN, P.P. and PERIEZ, . 2018. A machine-learning item recommendation system for video games. In In 2018 IEEE Conference on Computational Intelligence and Games (CIG), 1-4. IEEE.
  7. BOMHARDT, C. 2004. Newsrec, a svm-driven personal recommendation system for news websites. In In IEEE/WIC/ACM International Conference on Web Intelligence (WI’04), 545-548. IEEE.
  8. CASTILLO, C.2019. Fairness and transparency in ranking. In In ACM SIGIR Forum 52, 64-71. New York, NY, USA: ACM.
  10. social and context-aware mobile recommendation system for tourism. In Pervasive and Mobile Computing 38, 505-515.
  11. COVINGTON, P. ADAMS, J. and SARGIN, E. 2016. Deep neural networks for youtube recommendations. In
  12. In Proceedings of the 10th ACM conference on recommender systems, 191-198.
  13. DAI, H. WANG, Y. TRIVEDI, R. and SONG, L. 2016. Deep coevolutionary network: Embedding user and item features for recommendation. In arXiv preprint arXiv:1609.03675.
  14. DA’U, A. SALIM, N. RABIU, I. and OSMAN, A. 2020. Recommendation system exploiting aspect-based opinion mining with deep learning method. In Information Sciences 512, 1279-1292.
  15. FEUERBACH, J. LOEPP, B. and ZIEGLER, J. 2017. Enhancing an Interactive Recommendation System with Review-based Information Filtering. In In [email protected] RecSys, 2-9.
  16. GABRIEL DE SOUZA, P.M. JANNACH, D. and DA CUNHA, A.M. 2019. Contextual hybrid session-based news recommendation with recurrent neural networks. IEEE Access 7, 169185-169203.
  17. JANNACH, D. and HEGELICH, K. 2009. A case study on the effectiveness of recommendations in the mobile internet. In In Proceedings of the third ACM conference on Recommender systems, 205-208.
  18. KARIMI, S. SHAKERY, A. and VERMA, R. 2020. Online news media website ranking using user-generated content. In PJournal of Information Science, 0165551519894928.
  19. KONGSAKUN, K. and FUNG, C.C. 2012. Neural network modeling for an intelligent recommendation system supporting srm for universities in thailand. In WSEAS transactions on Computers 11, 34-44.
  20. KOTHARI, A.A. and PATEL, W.D. 2015. A novel approach towards context based recommendations using support vector machine methodology. In Procedia Computer Science 57, 1171-1178.
  21. LAU, C.W. 2011. News Recommendation System Using Logistic Regression and Naive Bayes Classifiers.
  22. LIAN, J. ZHANG, F. XIE, X. and SUN, G. 2017. CCCFNet: a content-boosted collaborative filtering neural network for cross domain recommender systems. In In Proceedings of the 26th international conference on World Wide Web companion, 817-818.
  23. LIU, J. DOLAN, P. and PEDERSEN, E.R. 2010. Personalized news recommendation based on click behavior. In Proceedings of the 15th international conference on Intelligent user interfaces, 31-40.
  24. MAI, J. FAN, Y. and SHEN, Y. 2009. A neural networks-based clustering collaborative filtering algorithm in e-commerce recommendation system. In In 2009 International Conference on Web Information Systems and Mining, 616-619. IEEE.
  25. MUKHERJEE, R. SAJJA, N. and SEN, S. 2003. A movie recommendation systeman application of voting theory in user modeling. In User Modeling and User-Adapted Interaction 13, 5-33.
  26. NASSAR, N. JAFAR, A. and RAHHAL, Y. 2020. A novel deep multi-criteria collaborative filtering model for recommendation system. In Knowledge-Based Systems 187, 104811.
  27. NEWMAN, N. FLETCHER, R. KALOGEROPOULOS, A. and NIELSEN, R. 2019. Reuters Institute digital news report 2019. In Reuters Institute for the Study of Journalism.
  28. OH, K.J. LEE, W.J. LIM, C.G. and CHOI, H.J. 2014. Personalized news recommendation using classified key- words to capture user preference. In 16th International Conference on Advanced Communication Technology, 1283-1287. IEEE.
  29. PARADARAMI, T.K. BASTIAN, N.D. and WIGHTMAN, J.L. 2017. A hybrid recommender system using artificial neural networks. In Expert Systems with Applications 83, 300-313.
  30. PARK, D.H. KIM, H.K. CHOI, I.Y. and KIM, J.K. 2012. A literature review and classification of recommender systems research. In Expert systems with applications 39, 10059-10072.
  31. PARK, K. LEE, J. and CHOI, J. 2017. Deep neural networks for news recommendations. In In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, 2255-2258.
  32. SATTAR, A. GHAZANFAR, M.A. and IQBAL, M. 2017. Building accurate and practical recommender system algorithms using machine learning classifier and collaborative filtering. In Arabian Journal for Science and Engineering, 42, 3229-3247.
  33. SHIRUDE, S.B. and KOLHE, S.R. 2018. Classification of Library Resources in Recommender System Using Machine Learning Techniques. In In Annual Convention of the Computer Society of India, 661-673. Springer, Singapore.
  34. THANGAVEL, S.K. BKARATKI, P.D. and SANKAR, A. 2017. Student placement analyzer: A recommen- dation system using machine learning. In In 2017 4th International Conference on Advanced Computing and Communication Systems (ICACCS), 1-5. IEEE.
  35. VENKATESAN, R. and SABARI, A. 2020. Issues in various recommender system in e-commerceA survey. In
  36. Journal of Critical Reviews, 7(7), 604-608.
  37. VON REISCHACH, F. GUINARD, D. MICHAHELLES, F. and FLEISCH, E. 2009. A mobile product rec-
  38. ommendation system interacting with tagged products. In In 2009 IEEE international conference on pervasive computing and communications, 1-6. IEEE.
  39. WANG, X. LUO, F. QIAN, Y. and RANZI, G. 2016. A personalized electronic movie recommendation system based on support vector machine and improved particle swarm optimization. In PloS one 11, e0165868.
  40. WANG, Z. YU, X. FENG, N. and WANG, Z. 2014. An improved collaborative movie recommendation system using computational intelligence. In Journal of Visual Languages & Computing 25, 667-675.
  41. WOHIDUZZAMAN, K. and ISMAIL, S. 2018. Recommendation System for Bangla News Article with Anaphora Resolution. In 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), 467-472. IEEE.
  42. XU, J.A. and ARAKI, K. 2006. A SVM-based personal recommendation system for TV programs. In In 2006 12th International Multi-Media Modelling Conference, 4-pp. IEEE.
  43. YAJUN, M. and SHENG, L. 2018. Application of LDA-LR in Personalized News Recommendation System. In In 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), 279-282. IEEE.
  44. ZHU, Q. ZHOU, X. SONG, Z.TAN, J.and GUO, L. 2019. Dan: Deep attention neural network for news recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence 33, 5973-5980.