Analysis and Research on Increased Probability Matrix Factorization Techniques in Collaborative Filtering

Authors

  • Kongari Mounika  M. Tech Scholar Department of CSE, NRI Institute of Technology Visadala, Guntur(Dt), Andhra Pradesh, India
  • B. V. N. Krishna Suresh  Assistant Professor Department of CSE, NRI Institute of Technology Visadala, Guntur(Dt), Andhra Pradesh, India

DOI:

https://doi.org/10.32628/IJSRSET207445

Keywords:

Matrix Factorization, Collaborative Filtering, Recommendation system, SVD, PMF

Abstract

The matrix factorization algorithms such as the matrix factorization technique (MF), singular value decomposition (SVD) and the probability matrix factorization (PMF) and so on, are summarized and compared. Based on the above research work, a kind of improved probability matrix factorization algorithm called MPMF is proposed in this paper. MPMF determines the optimal value of dimension D of both the user feature vector and the item feature vector through experiments. The complexity of the algorithm scales linearly with the number of observations, which can be applied to massive data and has very good scalability. Experimental results show that MPMF can not only achieve higher recommendation accuracy, but also improve the efficiency of the algorithm in sparse and unbalanced data sets compared with other related algorithms.

References

  1. L. Lu, M. Medo and C. H. Yeung, “Recommender systems”, Physics Reports, vol. 519, no. 1, (2012), pp. 149.
  2. J. S. Breese, D. Heckerman and C. Kadie “Empirical analysis of predictive algorithms for collaborative filtering”, Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence, Morgan Kaufmann Publishers Inc., (1998), pp. 43-52.
  3. F. Ricci, L. Rokach and B. Shapira , “Introduction to recommender systems handbook”, Recommender Systems Handbook, Springer US, (2011), pp. 1-35.
  4. M. Jahrer, A. Töscher and R. Legenstein , “Combining predictions for accurate recommender systems”, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, (2010), pp. 693-702.
  5. R. Bell, Y. Koren and C. Volinsky, “Modeling relationships at multiple scales to improve accuracy of large recommender systems”, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, (2007), pp. 95-104.
  6. L. M. De Campos, J. M. Fernández-Luna and J. F. Huete, “Combining content-based and collaborative recommendations: A hybrid approach based on Bayesian networks”, International journal of approximate reasoning, vol. 51, no. 7, (2010), pp. 785-799.
  7. H. Wu, Y. J. Wang and Z. Wang, “Two-Phase collaborative filtering algorithm based on co-clustering”, Journal of Software, vol. 21, no. 5, (2010), pp. 1042-1054.
  8. C. C. Aggarwal, J. L. Wolf and K. L. Wu, “Horting hatches an egg: A new graph-theoretic approach to collaborative filtering”, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, (1999), pp. 201-212.
  9. G. Adomavicius and A. Tuzhilin, “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions”, Knowledge and Data Engineering, IEEE Transactions on, vol. 17, no. 6, (2005), pp. 734-749.
  10. M. C. Chen, L. S. Chen and F. H. Hsu, “HPRS: A profitability based recommender system”, Industrial Engineering and Engineering Management, (2007), pp. 219-223.
  11. J. M. Yang and K. F. Li, “An inference-based collaborative filtering approach”, Dependable, Autonomic and Secure Computing, (2007), pp. 84-94.
  12. G. R. Xue, C. Lin and Q. Yang, “Scalable collaborative filtering using cluster-based smoothing”, Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, ACM, (2005), pp. 114-121.
  13. T. Iwata, K. Saito and T. Yamada, “Modeling user behavior in recommender systems based on maximum entropy”, Proceedings of the 16th international conference on World Wide Web. ACM, (2007), pp. 12811282.
  14. S. T. Park and D. M. Pennock, “Applying collaborative filtering techniques to movie search for better ranking and browsing”, Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, (2007), pp. 550-559.
  15. Q. Gu, J. Zhou and C. H. Q. Ding, “Collaborative Filtering: Weighted Nonnegative Matrix Factorization Incorporating User and Item Graphs”, SDM, (2010), pp. 199-210.
  16. S. Zhang, W. Wang and J. Ford, “Using singular value decomposition approximation for collaborative filtering”, E-Commerce Technology, (2005), pp. 257-264.
  17. D. D. Lee and H. S. Seung, “Learning the parts of objects by non-negative matrix factorization”, Nature, vol. 401, no. 6755, (1999), pp. 788-791.
  18. D. D. Tu, C. C. Shu and H. Y. Yu, “Using unified probabilistic matrix factorization for contextual advertisement recommendation”, Journal of Software, vol. 24, no. 3, (2013), pp. 454-464.
  19. B. Sarwar, G. Karypis and J. Konstan, “Application of dimensionality reduction in recommender system-a case study”, MINNESOTA UNIV MINNEAPOLIS DEPT OF COMPUTER SCIENCE, (2000).
  20. S. Funk, “Netflix update: Try this at home”, http://sifter.org/~simon/journal/20061211.html, (2006).
  21. T. Chen, W. Zhang and Q. Lu, “SVDFeature: A Toolkit for Feature-based Collaborative Filtering”, Journal of Machine Learning Research, vol. 13, (2012), pp. 3619-3622.
  22. R. M. Bell and Y. Koren, “Lessons from the Netflix prize challenge”, ACM SIGKDD Explorations Newsletter, vol. 9, no. 2, (2007), pp. 75-79.
  23. Y. Koren, R. Bell and C. Volinsky, “Matrix factorization techniques for recommender systems”, Computer, vol. 42, no. 8, (2009), pp. 30-37.
  24. A. Mnih and R. Salakhutdinov, “Probabilistic matrix factorization”, Advances in neural information processing systems, (2007), pp. 1257-1264.
  25. Y. N. Fang, Y. F. Guo and X. T. Ding, “An improved singular value decomposition recommender algorithm based on local structures”, Journal of Electronics & Information Technology, vol. 35, no. 6, (2013), pp. 1284-1289.
  26. N. D. Lawrence and R. Urtasun, “Non-linear matrix factorization with Gaussian processes”, Proceedings of the 26th Annual International Conference on Machine Learning, ACM, (2009), pp. 601-608.
  27. R. Salakhutdinov and A. Mnih, “Bayesian probabilistic matrix factorization using Markov chain Monte Carlo”, Proceedings of the 25th international conference on Machine learning. ACM, (2008), pp. 880-887.

International Journal of Scientific Research in Science, Engineering and Technology

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Published

2020-08-30

Issue

Volume 7, Issue 4, July-August-2020

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Research Articles

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How to Cite

[1]
Kongari Mounika, B. V. N. Krishna Suresh "Analysis and Research on Increased Probability Matrix Factorization Techniques in Collaborative Filtering" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 7, Issue 4, pp.182-187, July-August-2020. Available at doi : https://doi.org/10.32628/IJSRSET207445