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A Survey of Recommender Systems Based on Semi-supervised Learning

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International Conference on Innovative Computing and Communications

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1394))

Abstract

A recommender system is considered to provide user-specific recommendations of products or any kind of services depending upon different sectors such as business, marketing, government, education, health, etc. It is a tool or method that facilitates to manage any kind of online overloading problem between customers and organizations. Machine learning is being utilized to provide the intelligent recommender systems that are able to learn the behaviour based on historical data. A lot of work and related software based on machine learning have been proposed recently by using supervised and unsupervised learning in this area by using a collaborative, content-based and hybrid approach. But the existing work utilizes the supervised and unsupervised methods in proposing several recommender systems which are suffering from many shortcomings. The main disadvantage that we observe is that supervised and unsupervised learning techniques become incompatible and useless when data is dispersed and completely labelled data is not available. Therefore, recently a new concept of semi-supervised learning has been used to build recommender systems in various applications. In semi-supervised learning, little supervision is provided to unlabelled data by using available partial labels, which is also a cost-effective approach. Thus, it is necessary to provide a high-quality and instructive survey that reveals the importance of semi-supervised learning techniques along with collaborative content and hybrid filtering techniques, contributing to recommender systems. Hence, this paper presents a recent survey in the development of recommender systems.

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References

  1. I. Portugal, P. Alencar, D. Cowan, The use of machine learning algorithms in recommender systems: a systematic review. Expert Syst. Appl. 97, 205–227 (2018)

    Article  Google Scholar 

  2. K. Anwar, J. Siddiqui, S.S. Sohail, Machine learning-based book recommender system: a survey and new perspectives. Int. J. Intell. Inf. Database Syst. 13(2–4), 231–248 (2020)

    Google Scholar 

  3. L. Jie, W. Dianshuang, M. Mao, W. Wang, G. Zhang, Recommender system application developments: a survey. Decis. Support Syst. 74, 12–32 (2015)

    Article  Google Scholar 

  4. C. Panagiotakis, H. Papadakis, P. Fragopoulou, Unsupervised and supervised methods for the detection of hurriedly created profiles in recommender systems. Int. J. Mach. Learn. Cybern. (2020)

    Google Scholar 

  5. A. Da’u, N. Salim, I. Rabiu, A. Osman, Weighted aspect-based opinion mining using deep learning for recommender system. Expert Syst. Appl. 140 (2020)

    Google Scholar 

  6. R. Kiran, P. Kumar, B. Bhasker, DNNRec: a novel deep learning based hybrid recommender system. Expert Syst. Appl. 144, 113054 (2020)

    Google Scholar 

  7. R.H. Singh, S. Maurya, T. Tripathi, T. Narula, G. Srivastav, Movie recommendation system using cosine similarity and KNN. Int. J. Eng. Adv. Technol. 9(5), 556–559 (2020)

    Google Scholar 

  8. D. Contreras, M. Salamó, A cognitively inspired clustering approach for critique-based recommenders. Cogn. Comput. 12(2), 428–441 (2020)

    Article  Google Scholar 

  9. A. Mustaqeem, S.M. Anwar, M. Majid, A modular cluster based collaborative recommender system for cardiac patients. Artif. Intell. Med. 102, 101761 (2020)

    Google Scholar 

  10. S.S.V.R. Kumar Addagarla, A. Amalanathan, Probabilistic unsupervised machine learning approach for a similar image recommender system for e-commerce. Symmetry 12(11), 1783 (2020)

    Google Scholar 

  11. D.C.G. Putri, J.-S. Leu, P. Seda, Design of an unsupervised machine learning-based movie recommender system. Symmetry 12(2), 185 (2020)

    Article  Google Scholar 

  12. M. Atif, J. Siddiqui, F. Talib, An overview of diabetes mellitus prediction through machine learning approaches, in 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom) (IEEE, 2019), pp. 1145–1150

    Google Scholar 

  13. Z. Hu, G. Xu, X. Zheng, J. Liu, Z. Li, Q.Z. Sheng, W. Lian, H. Xian, SSL-SVD: semi-supervised learning-based sparse trust recommendation. ACM Trans. Internet Technol. (TOIT) 20(1), 1–20 (2020)

    Google Scholar 

  14. O.A. Alzubi, J.A. Alzubi, M. Alweshah, I. Qiqieh, S. Al-Shami, M. Ramachandran, An optimal pruning algorithm of classifier ensembles: dynamic programming approach. Neural Comput. Appl. 1–17 (2020)

    Google Scholar 

  15. D. Gupta, J.J.P.C. Rodrigues, S. Sundaram, A. Khanna, V. Korotaev, V.H.C. de Albuquerque, Usability feature extraction using modified crow search algorithm: a novel approach. Neural Comput. Appl. 1–11 (2018)

    Google Scholar 

  16. M. Atif, J. Siddiqui, F. Talib, S.S. Sohail, Applications of machine learning techniques for disease diagnosis: a review. J. Crit. Rev. 7(17), 2652–2661 (2020)

    Google Scholar 

  17. R. Mishra, S. Rathi, Efficient and scalable job recommender system using collaborative filtering, in ICDSMLA 2019 (Springer, Berlin, 2020), pp. 842–856

    Google Scholar 

  18. X. Li, G. Cong, X.-L. Li, T.-A.N. Pham, S. Krishnaswamy, Rank-GeoFM: a ranking based geographical factorization method for point of interest recommendation, in SIGIR 2015—Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, ed. by A. Moffat, M. Lalmas, B. Ribeiro-Neto. United States of America, 2015, pp. 433–442. ACM International Conference on Research and Development in Information, Association for Computing Machinery (ACM), Retrieval 2015, SIGIR 2015; Conference date: 09-08-2015 through 13-08-2015

    Google Scholar 

  19. R. Logesh, V. Subramaniyaswamy, D. Malathi, N. Sivaramakrishnan, V. Vijayakumar, Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Comput. Appl. 32(7), 2141–2164 (2020)

    Article  Google Scholar 

  20. J. Bobadilla, S. Alonso, A. Hernando, Deep learning architecture for collaborative filtering recommender systems. Appl. Sci. 10(7), 2441 (2020)

    Article  Google Scholar 

  21. R. Lara-Cabrera, Á. González-Prieto, F. Ortega, Deep matrix factorization approach for collaborative filtering recommender systems. Appl. Sci. 10(14), 4926 (2020)

    Article  Google Scholar 

  22. A.F.T. Cruz, A.D. Coronel, Towards developing a content-based recommendation system for classical music, in Information Science and Applications (Springer, Berlin, 2020), pp. 451–462

    Google Scholar 

  23. K.R. Raheem, I.H. Ali, Content-based recommender system improvement using hybrid technique, in 2020 1st Information Technology to Enhance E-Learning and Other Application (IT-ELA) (IEEE, 2020), pp. 54–59

    Google Scholar 

  24. S. Amara, R.R. Subramanian, Collaborating personalized recommender system and content-based recommender system using text corpus, in 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS) (IEEE, 2020), pp. 105–109

    Google Scholar 

  25. M. Arafeh, P. Ceravolo, A. Mourad, E. Damiani, E. Bellini, Ontology based recommender system using social network data. Future Gener. Comput. Syst. 115, 769–779 (2020)

    Article  Google Scholar 

  26. B. Cami, H. Hassanpour, H. Mashayekhi, User preferences modeling using dirichlet process mixture model for a content-based recommender system. Knowl.-Based Syst. 163, 09 (2018)

    Google Scholar 

  27. R. Burke, Robin, Hybrid web recommender systems. 4321, 01 (2007)

    Google Scholar 

  28. A.N. Ngaffo, W. El Ayeb, Z. Choukair, A Bayesian inference based hybrid recommender system. IEEE Access 8, 101682–101701 (2020)

    Google Scholar 

  29. J.K. Tarus, Z. Niu, A. Yousif, A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Gener. Comput. Syst. 72, 37–48 (2017)

    Google Scholar 

  30. Y. Liu, S. Wang, M. Shahrukh Khan, J. He, A novel deep hybrid recommender system based on auto-encoder with neural collaborative filtering. Big Data Min. Anal. 1(3), 211–221 (2018)

    Google Scholar 

  31. K. Wagstaff, C. Cardie, Clustering with instance-level constraints. AAAI/IAAI 1097, 577–584 (2000)

    Google Scholar 

  32. E. Amid, A. Gionis, A. Ukkonen, A Kernel-learning approach to semi-supervised clustering with relative distance comparisons, in Joint European Conference on Machine Learning and Knowledge Discovery in Databases (Springer, Berlin, 2015), pp. 219–234

    Google Scholar 

  33. C. Yang, L. Bai, C. Zhang, Q. Yuan, J. Han, Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation, in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2017), pp. 1245–1254

    Google Scholar 

  34. S. Roy, M. Sharma, S.K. Singh, Movie recommendation system using semi-supervised learning, in 2019 Global Conference for Advancement in Technology (GCAT) (IEEE, 2019), pp. 1–5

    Google Scholar 

  35. J. Zhou, W. Chen, G. Wu, J. Wei, SemiTagRec: a semi-supervised learning based tag recommendation approach for docker repositories, in International Conference on Software and Systems Reuse (Springer, Berlin, 2019), pp. 132–148

    Google Scholar 

  36. S. Kang, J. Hwang, D. Lee, H. Yu, Semi-supervised learning for cross-domain recommendation to cold-start users, in Proceedings of the 28th ACM International Conference on Information and Knowledge Management (2019), pp. 1563–1572

    Google Scholar 

  37. C. Chen, K.C.-C. Chang, Q. Li, X. Zheng, Semi-supervised learning meets factorization: learning to recommend with chain graph model. ACM Trans. Knowl. Disc. Data (TKDD) 12(6), 1–24 (2018)

    Google Scholar 

  38. Y. Zhang, X. Liu, W. Liu, C. Zhu, Hybrid recommender system using semi-supervised clustering based on gaussian mixture model, in 2016 International Conference on Cyberworlds (CW) (IEEE, 2016), pp. 155–158

    Google Scholar 

  39. W. Yu, X. Lin, J. Ge, W. Ou, Z. Qin, Semi-supervised collaborative filtering by text-enhanced domain adaptation, in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2020), pp. 2136–2144

    Google Scholar 

  40. M. Goyani, N. Chaurasiya, A review of movie recommendation system: limitations, survey and challenges. ELCVIA: Electron. Lett. Comput. Vis. Image Anal. 19(3), 0018–0037 (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Aligarh Muslim University, Aligarh, India

    Aslam Hasan Khan & Jamshed Siddqui

  2. Jamia Hamdard University, New Delhi, India

    Shahab Saquib Sohail

Authors
  1. Aslam Hasan Khan
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  2. Jamshed Siddqui
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  3. Shahab Saquib Sohail
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Editor information

Editors and Affiliations

  1. Maharaja Agrasen Institute of Technology, Delhi, India

    Ashish Khanna

  2. Department of Computer Science Engineering, Maharaja Agrasen Institute of Technology, Rohini, Delhi, India

    Deepak Gupta

  3. Rajnagar Mahavidyalaya, Birbhum, India

    Siddhartha Bhattacharyya

  4. Computer and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  5. Department of Computer Science, Shaheed Sukhdev College of Business Studies, Rohini, Delhi, India

    Sameer Anand

  6. Department of Computer Science, Shaheed Sukhdev College of Business Studies, Rohini, Delhi, India

    Ajay Jaiswal

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Khan, A.H., Siddqui, J., Sohail, S.S. (2022). A Survey of Recommender Systems Based on Semi-supervised Learning. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1394. Springer, Singapore. https://doi.org/10.1007/978-981-16-3071-2_27

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