Vol. 21 No. 3 (2022): Mapana Journal of Sciences
Research Articles

Boltzmann Machines Associated Recommender System: A Review

PDF
  • Dheeraj Kumar Sahni,
  • Dhiraj Khurana
Dheeraj Kumar Sahni
Maharshi Dayanand University, Rohtak.
Bio
Dhiraj Khurana
Maharshi Dayanand University, Rohtak.
Bio

Published 2022-07-01

Keywords

  • Boltzmann Machine (BM),
  • Neural Network,
  • Recommender System

Copyright (c) 2022

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Abstract

Nowadays, the information on the internet presents explosive growth; similar information from the space of information available overwhelms users. Collaborative filtering is one of the alternatives used for solving this problem. Recommendations are the need of daily life to choose the better alternative from the given choices. Everyone uses recommendations to approach the good items and services in this interconnected world. The recommender system is a software solution to make this process easy. This article presents the application of Boltzmann machines in recommendation systems for the last twenty years.

PDF

References

  1. Abbas, M.A., Ajayi, S., Bilal, M. et al. A deep learning approach for context-aware citation recommendation using rhetorical zone classification and similarity to overcome cold-start problem. J Ambient Intell Human Comput (2022). https://doi.org/10.1007/s12652-022-03899-6.
  2. Aditya, P. H., Budi, I., & Munajat, Q. (2017). A comparative analysis of memory-based and model-based collaborative filtering on the implementation of recommender system for E-commerce in indonesia: A case study PT X. Paper presented at the 2016 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2016, 303-308. doi:10.1109/ICACSIS.2016.7872755
  3. Adomavičius, G., & Tuzhilin, A. (2005). Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749. doi:10.1109/TKDE.2005.99
  4. Asemi, A., Asemi, A., Ko, A., & Alibeigi, A. (2022). An integrated model for evaluation of big data challenges and analytical methods in recommender systems. Journal of Big Data, 9(1) doi:10.1186/s40537-022-00560-z.
  5. Ackley, D.H., Hinton, G.E., & Sejnowski, T.J. (1985). A Learning Algorithm for Boltzmann Machines. Cogn. Sci., 9, 147-169, doi.org/10.1016/S0364-0213(85)80012-4.
  6. Biswal, A., Borah, M. D., & Hussain, Z. (2021). Music recommender system using restricted boltzmann machine with implicit feedback doi:10.1016/bs.adcom.2021.01.00.
  7. Behera, D. K., Das, M., Swetanisha, S., & Sethy, P. K. (2021). Hybrid model for movie recommendation system using content K-nearest neighbors and restricted boltzmann machine. Indonesian Journal of Electrical Engineering and Computer Science, 23(1), 445-452. doi:10.11591/ijeecs.v23.i1.pp445-452.
  8. Behera, D. K., Das, M., Swetanisha, S., & Naik, B. (2018). Collaborative filtering using restricted boltzmann machine and fuzzy C-means doi:10.1007/978-981-10-7871-2_69.
  9. Batmaz, Z., Yurekli, A., Bilge, A., & Kaleli, C. (2019). A review on deep learning for recommender systems: Challenges and remedies. Artificial Intelligence Review, 52(1), 1-37. doi:10.1007/s10462-018-9654-y.
  10. Chen, Z., Ma, W., Dai, W., Pan, W., & Ming, Z. (2020). Conditional restricted boltzmann machine for item recommendation. Neurocomputing, 385, 269-277. doi:10.1016/j.neucom.2019.12.088.
  11. Chen, J., Cheng, S., Xie, H., Wang, L., & Xiang, T. (2018). Equivalence of restricted boltzmann machines and tensor network states. Physical Review B, 97(8) doi:10.1103/PhysRevB.97.085104.
  12. Cho, K. H., Raiko, T., & Ilin, A. (2013). Gaussian-bernoulli deep boltzmann machine. Paper presented at the Proceedings of the International Joint Conference on Neural Networks, doi:10.1109/IJCNN.2013.670683.
  13. Deshmukh, V. M., & Shukla, S. (2021). Content-restricted boltzmann machines for diet recommendation doi:10.1007/978-981-16-4486-3_12.
  14. Danilova, V., & Ponomarev, A. (2022). Hybrid recommender systems: The review of state-of-the-art research and applications. Proceedings of the 20th Conference of FRUCT Association, , 572-578.
  15. D. H. Tran, Q. Z. Sheng, W. E. Zhang, S. A. Hamad, N. L. D. Khoa and N. H. Tran, "Deep Conversational Recommender Systems: Challenges and Opportunities," in Computer, vol. 55, no. 4, pp. 30-39, April 2022, doi: 10.1109/MC.2020.3045426.
  16. Feuerverger, A., He, Y., & Khatri, S. (2012). Statistical significance of the netflix challenge. Statistical Science, 27(2), 202-231. doi:10.1214/11-STS368.
  17. G.E. Hinton, Boltzmann machine, Scholarpedia 2 (5) (2007) doi:10.4249/scholarpedia.1668.
  18. Gunawardana, A., & Meek, C. (2009). A unified approach to building hybrid recommender systems. Paper presented at the RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems, 117-124. doi:10.1145/1639714.1639735.
  19. Gunawardana, A., & Meek, C. (2008). Tied boltzmann machines for cold start recommendations. Paper presented at the RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems, 19-26. doi:10.1145/1454008.1454013.
  20. Georgiev, K., & Nakov, P. (2013). A non-IID framework for collaborative filtering with restricted boltzmann machines. Paper presented at the 30th International Conference on Machine Learning, ICML 2013, (PART 3) 2185-2193.
  21. Harshvardhan, G. M., Gourisaria, M. K., Rautaray, S. S., & Pandey, M. (2021). UBMTR: Unsupervised boltzmann machine-based time-aware recommendation system. Journal of King Saud University - Computer and Information Sciences, doi:10.1016/j.jksuci.2021.01.017.
  22. Hinton, Geoffrey & Sejnowski, Terrence. (1983). Optimal perceptual inference. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 448-453.
  23. Hazrati, N., & Elahi, M. (2021). Addressing the new item problem in video recommender systems by incorporation of visual features with restricted boltzmann machines. Expert Systems, 38(3) doi:10.1111/exsy.12645.
  24. He, F., & Li, N. (2017). Restricted boltzmann machine based on item category for collaborative filtering. Paper presented at the Proceedings - 2017 International Conference on Computer Technology, Electronics and Communication, ICCTEC 2017, 756-760. doi:10.1109/ICCTEC.2017.00167.
  25. He, J. -., & Ma, B. (2016). Based on real-valued conditional restricted boltzmann machine and social network for collaborative filtering. Jisuanji Xuebao/Chinese Journal of Computers, 39(1), 183-195. doi:10.11897/SP.J.1016.2016.00183.
  26. Hinton, G. E. (2012). A practical guide to training restricted boltzmann machines doi:10.1007/978-3-642-35289-8_32
  27. Idrissi, N., Hourrane, O., Zellou, A., & Benlahmar, E. H. (2019). A restricted boltzmann machine-based recommender system for alleviating sparsity issues. Paper presented at the ICSSD 2019 - International Conference on Smart Systems and Data Science, doi:10.1109/ICSSD47982.2019.9003149.
  28. Jahrer, M., Töscher, A., & Legenstein, R. (2010). Combining predictions for accurate recommender systems. Paper presented at the Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 693-701. doi:10.1145/1835804.1835893.
  29. Jawaheer, G., Szomszor, M., & Kostkova, P. (2010). Comparison of implicit and explicit feedback from an online music recommendation service. Paper presented at the Proceedings of the 1st International Workshop on Information Heterogeneity and
  30. Fusion in Recommender Systems, HetRec 2010, Held at the 4th ACM Conference on Recommender Systems, RecSys 2010, 47-51. doi:10.1145/1869446.1869453.
  31. Jiang, W., Zhao, W., Du, L., Zhang, K., & Yu, M. (2023). Product perceptual similarity evaluation: From attributive error to human knowledge hierarchy. Journal of Computing and Information Science in Engineering, 23(2) doi:10.1115/1.4054305
  32. Kuo, R. J., & Chen, J. T. (2020). An application of differential evolution algorithm-based restricted boltzmann machine to recommendation systems. Journal of Internet Technology, 21(3), 701-712. doi:10.3966/160792642020052103008.
  33. Liu, Y., Tong, Q., Du, Z., & Hu, L. (2014). Content-boosted restricted boltzmann machine for recommendation doi:10.1007/978-3-319-11179-7_97.
  34. Liu, X., Ouyang, Y., Rong, W., & Xiong, Z. (2015). Item category aware conditional restricted boltzmann machine based recommendation doi:10.1007/978-3-319-26535-3_69.
  35. Lee, Y. -., Son, J., Kim, T., Park, D., & Kim, S. -. (2021). Exploiting uninteresting items for effective graph-based one-class collaborative filtering. Journal of Supercomputing, 77(7), 6832-6851. doi:10.1007/s11227-020-03573-8.
  36. Mansur, F., Patel, V., & Patel, M. (2022). A review on recommender systems. 2017 International Conference on Innovations in Information, Embedded and Communication Systems
  37. Negi, R., & Patil, A. B. (2021). Deep collaborative filtering based recommendation system. Paper presented at the 2021 12th International Conference on Computing Communication and Networking Technologies, ICCCNT 2021, doi:10.1109/ICCCNT51525.2021.9579848.
  38. Nair, V., & Hinton, G. E. (2010). Rectified linear units improve restricted boltzmann machines. Paper presented at the ICML 2010 - Proceedings, 27th International Conference on Machine Learning, 807-814.
  39. Nathanson, T., Bitton, E., & Goldberg, K. (2007). Eigentaste 5.0: Constant-time adaptability in a recommender system using item clustering. Paper presented at the RecSys'07: Proceedings of the 2007 ACM Conference on Recommender Systems, 149-152. doi:10.1145/1297231.1297258
  40. Peterson, C. and Anderson, J.R. (1987), A mean field theory learning algorithm for neural networks. Complex Systems, 1(5):995--1019.
  41. Parra, D., Amatriain, X. (2011). Walk the Talk. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds) User Modeling, Adaption and Personalization. UMAP 2011. Lecture Notes in Computer Science, vol 6787. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22362-4_22.
  42. Polonioli, A. (2021). The ethics of scientific recommender systems. Scientometrics, 126(2), 1841-1848. doi:10.1007/s11192-020-03766-1.
  43. Pramod, D., & Bafna, P. (2022). Conversational recommender systems techniques, tools, acceptance, and adoption: A state of the art review. Expert Systems with Applications, 203 doi:10.1016/j.eswa.2022.117539
  44. R. R. Salakhutdinov and G. E. Hinton. Deep Boltzmann machines. In Proceedings of the International Conference on Artificial Intelligence and Statistics, volume 12, 2009.
  45. Raza, S., & Ding, C. (2022). News recommender system: A review of recent progress, challenges, and opportunities. Artificial Intelligence Review, 55(1), 749-800. doi:10.1007/s10462-021-10043-x.
  46. Roy, D., & Dutta, M. (2022). A systematic review and research perspective on recommender systems. Journal of Big Data, 9(1) doi:10.1186/s40537-022-00592-5
  47. Salakhutdinov, R., Mnih, A., & Hinton, G. (2007). Restricted boltzmann machines for collaborative filtering. Paper presented at the ACM International Conference Proceeding Series, , 227 791-798. doi:10.1145/1273496.1273596.
  48. Sejnowski, Terrence. (1987). Higher-Order Boltzmann Machines. 151. 10.1063/1.36246, Volume 13, Issue 3, 2000, Pages 329-335, doi.org/10.1016/S0893-6080(00)00011-3.
  49. Salakhutdinov, R.R. & Hinton, G.E.. (2012). A better way to pretrain Deep Boltzmann Machines. Advances in Neural Information Processing Systems. 3. 2447-2455.
  50. Teppan, E. C. (2008). Implications of psychological phenomenons for recommender systems. Paper presented at the RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems, 323-326. doi:10.1145/1454008.1454063.
  51. Verma, S., Patel, P., & Majumdar, A. (2018). Collaborative filtering with label consistent restricted boltzmann machine. Paper presented at the 2017 9th International Conference on Advances in Pattern Recognition, ICAPR 2017, 58-63.
  52. doi:10.1109/ICAPR.2017.8593079.
  53. Welling, M., Rosen-Zvi, M., and Hinton, G. E. (2005). Exponential family harmoniums with an application to information retrieval. Advances in Neural Information Processing Systems 17, pages 1481-1488. MIT Press, Cambridge, MA.
  54. Wang, Y. -., Tang, W. -., Yang, X. -., Wu, Y. -., & Chen, F. -. (2019). An efficient method for autoencoder-based collaborative filtering. Concurrency and Computation: Practice and Experience, 31(23) doi:10.1002/cpe.4507.
  55. Wang, W., Yin, H., Huang, Z., Sun, X., & Hung, N. Q. V. (2018). Restricted boltzmann machine based active learning for sparse recommendation doi:10.1007/978-3-319-91452-7_7.
  56. Wang, L. (2017). Exploring cluster monte carlo updates with boltzmann machines. Physical Review E, 96(5) doi:10.1103/PhysRevE.96.051301.
  57. Y. Hu, Y. Koren and C. Volinsky, "Collaborative Filtering for Implicit Feedback Datasets," 2008 Eighth IEEE International Conference on Data Mining, 2008, pp. 263-272, doi: 10.1109/ICDM.2008.22.