Collaborative Filtering Using Restricted Boltzmann Machine and Fuzzy C-means

  • Dayal Kumar Behera
  • Madhabananda Das
  • Subhra Swetanisha
  • Bighnaraj Naik
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 710)

Abstract

Recommender system is valuable to find items as per users’ taste from a large volume of items. Various popular techniques to perform personalized recommendations are content based, collaborative, and hybrid recommender. Collaborative filtering is widely used in this domain which can be of memory based or model based. The datasets used in recommender systems are very often sparse. Hence, accurate prediction can be made by grouping users/items into cluster. In this paper, an attempt is made to cluster the users using FCM clustering algorithm, and then, RBM is used to predict the user’s preferences. Experiment is carried out on MovieLens benchmark dataset. The results depict the performance of using both FCM and RBM to build the model for recommendation.

Keywords

Collaborative filtering Restricted Boltzmann Machine User-based filtering Movie recommendation 

Notes

Acknowledgements

The authors would like to express thanks to all the reviewers for valuable comments and suggestions.

References

  1. 1.
    Silva, E., Camilo-Junior, C., Pascoal, L., Rosa, T.: An evolutionary approach for combining results of recommender systems techniques based on collaborative filtering. Science Direct. Expert Systems With Applications 53 (2016) 204–218.Google Scholar
  2. 2.
    Bu, J., Shen, X., Xu, B., Chen, C., He, X., Cai, D.: Improving Collaborative Recommendation via User-Item Subgroups. IEEE Transactions on Knowledge and Data Engineering. vol. 28. no. 9, (2016).Google Scholar
  3. 3.
    Salehi, M., Kamalabadi, I., Ghoushchi, M.: An Effective Recommendation Framework for Personal Learning Environments Using a Learner Preference Tree and a GA. IEEE Transactions on Learning Technologies. vol. 6. no. 4. (2013).Google Scholar
  4. 4.
    Mendoza, L., García, R., González, A., Hernández, G., Zapater, J., RecomMetz: A context-aware knowledge-based mobile recommender system for movie showtimes. Science Direct. Expert Systems with Applications 42 (2015) 1202–1222.Google Scholar
  5. 5.
    Wei, S., Zheng, X., Chen, D., Chen, C.: A hybrid approach for movie recommendation via tags and ratings. Science Direct. Electronic Commerce Research and Applications 18 (2016) 83–94.Google Scholar
  6. 6.
    Nilashi, M., Ibrahim, O., Ithnin, N.: Hybrid recommendation approaches for multi-criteria collaborative filtering. Science Direct. Expert Systems with Applications 41 (2014) 3879–3900.Google Scholar
  7. 7.
    Salakhutdinov, R., Mnih, A., Hinton, G: Restricted Boltzmann Machines for collaborative filtering. In Proceedings of the 24th International Conference on Machine Learning. (2007).Google Scholar
  8. 8.
    Salah, A., Rogovschi, N., Nadif, M.: A dynamic collaborative filtering system via a weighted clustering approach. Science Direct. Neurocomputing 175 (2016) 206–215.Google Scholar
  9. 9.
    Goldberg. D., Nichols. D., Oki B. M., Terry. D., Using collaborative filtering to weave an Information tapestry. Commun. ACM 35 (1992) 61–70.Google Scholar
  10. 10.
    Ortega, F., Hernando, A., Bobadilla, J., Hyung Kang, J.: Recommending items to group of users using Matrix Factorization based Collaborative Filtering. Science Direct. Information Sciences 345 (2016) 313–324.Google Scholar
  11. 11.
    Koohi, H., Kiani, K.: User based Collaborative Filtering using fuzzy C-means. Science Direct. Measurement 91 (2016) 134–139.Google Scholar
  12. 12.
    Moreno, M., Segrera, S., López,V., Muñoz, M.: Web mining based framework for solving usual problems in recommender systems. A case study for movies’ recommendation. Science Direct. Neurocomputing 176 (2016) 72–80.Google Scholar
  13. 13.
    Chen, M., Teng, C., Chang, P.: Applying artificial immune systems to collaborative filtering for movie recommendation. Science Direct. Advanced Engineering Informatics 29 (2015) 830–839.Google Scholar
  14. 14.
    Wang, Z., Yu, X., Feng, N., Wang, Z.: An improved collaborative movie recommendation system using computational intelligence. Science Direct. Journal of Visual Languages and Computing 25 (2014) 667–675.Google Scholar
  15. 15.
    Zahra, S., Ghazanfar, M., Khalid, A., Azam, A., Naeem, U., Prugel-Bennett, A.: Novel centroid selection approaches for KMeans-clustering based recommender systems. ScienceDirect. Information Sciences 320 (2015) 156–189.Google Scholar
  16. 16.
    Zenebe, A., Zhou, L., Norcio, A.: User preferences discovery using fuzzy models. ScienceDirect. Fuzzy Sets and Systems 161 (2010) 3044–3063.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Dayal Kumar Behera
    • 1
    • 2
  • Madhabananda Das
    • 1
  • Subhra Swetanisha
    • 2
  • Bighnaraj Naik
    • 3
  1. 1.Department of CSEKIIT UniversityBhubaneswarIndia
  2. 2.Department of CSETrident Academy of TechnologyBhubaneswarIndia
  3. 3.Department of Computer ApplicationVSSUTBurlaIndia

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