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Applying memetic algorithm-based clustering to recommender system with high sparsity problem

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Abstract

A new recommendation method was presented based on memetic algorithm-based clustering. The proposed method was tested on four highly sparse real-world datasets. Its recommendation performance is evaluated and compared with that of the frequency-based, user-based, item-based, k-means clustering-based, and genetic algorithm-based methods in terms of precision, recall, and F1 score. The results show that the proposed method yields better performance under the new user cold-start problem when each of new active users selects only one or two items into the basket. The average F1 scores on all four datasets are improved by 225.0%, 61.6%, 54.6%, 49.3%, 28.8%, and 6.3% over the frequency-based, user-based, item-based, k-means clustering-based, and two genetic algorithm-based methods, respectively.

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References

  1. ADOMAVICIUS G, TUZHILIN A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions [J]. IEEE Trans on Knowledge and Data Engineering, 2005, 17(6): 734–749.

    Article  Google Scholar 

  2. RESNICK P, VARIAN H R. Recommender systems [J]. Communications of the ACM, 1997, 40(3): 56–58.

    Article  Google Scholar 

  3. SUMMUT C, WEBB G I. Encyclopedia of machine learning [M]. Berlin: Springer, 2010: 829–838.

    Book  Google Scholar 

  4. MELVILLE P, MOONEY R J, NAGARAJAN R. Content-boosted collaborative filtering for improved recommendations [C]// Proc of Intl Conf on Artificial Intell. CA, USA: American Association Intelligence, 2002: 187–192.

    Google Scholar 

  5. WENG S S, LIU M J. Feature-based recommendations for one-to-one marketing [J]. Expert Syst with App, 2004, 26(4): 493–508.

    Article  Google Scholar 

  6. SALTER J, ANTONOPOULOS A. Cinema screen recommender agent: Combining collaborative and content-based filtering [J]. IEEE Mag on Intell Syst, 2006, 21(1): 35–41.

    Article  Google Scholar 

  7. ADOMAVICIUS G, KWON Y. New recommendation techniques for multicriteria rating systems [J]. IEEE Mag on Intell Syst, 2007, 22(3): 48–55.

    Article  Google Scholar 

  8. MOONEY R J, ROY L. Content-based book recommending using learning for text categorization [C]// Proc of ACM Conf on Digital Libraries New York, USA: ACM, 2000: 195–204.

    Chapter  Google Scholar 

  9. RICCI F, WERTHNER H. Case-based querying for travel planning recommendation [J]. Information Tech and Tourism, 2002, 4(3): 215–226.

    Article  Google Scholar 

  10. LINDEN G, SMITH B, YORK J. Amazon.com recommendations item-to-item collaborative filtering [J]. IEEE Mag on Internet Computing, 2003, 7(1): 76–80.

    Article  Google Scholar 

  11. ZHANG F, CHANG H. A collaborative filtering algorithm employing genetic clustering to ameliorate the scalability issue [C]// Proc of Intl Conf on E-Business Eng. Piscataway, USA: IEEE, 2006: 331–338.

    Google Scholar 

  12. LUO X, OUYANG Y, XIONG Z. Improving neighborhood based collaborative filtering via integrated folksonomy information [J]. Pattern Recognition Letters, 2012, 33(3): 263–270.

    Article  Google Scholar 

  13. SARWAR B, KARYPIS G, KONSTAN J, RIEDL J. Analysis of recommendation algorithms for e-commerce [C]// Proc of Conf on Electron Commerce New York, USA: ACM, 2000: 158–167.

    Google Scholar 

  14. ALTINGOVDE I S, SUBAKAN O N, ULUSOY O. Cluster searching strategies for collaborative recommendation system [J]. Info Proc & Manage, 2013, 49(3): 688–697.

    Article  Google Scholar 

  15. BREESE J S, HECKERMAN D, KADIE C. Empirical analysis of predictive algorithms for collaborative filtering [C]// Proc of Conf on Uncertainty in Artificial Intelligence. San Fracisco, USA: Morgan Kaufmann Publishers Inc, 1998: 43–52.

    Google Scholar 

  16. SARWAR B, KARYPIS G, KONSTAN J, RIEDL J. Item-based collaborative filtering recommendation algorithms [C]// Proc of Intl Conf on World Wide Web New York, USA: ACM, 2001: 285–295.

    Google Scholar 

  17. GOOD N, SCHAFER J B, KONSTAN J A, BORCHERS, A, SARWAR B, HERLOCKER J, RIEDL J. Combining collaborative filtering with personal agents for better recommendations [C]// Proc of Nat Conf on Artificial Intell. CA, USA: American Association Intelligence, 1999: 439–446.

    Google Scholar 

  18. CAO Y, LI Y. An intelligent fuzzy-based recommendation system for consumer electronic products [J]. Expert Syst With App, 2007, 33(1): 230–240.

    Article  Google Scholar 

  19. PARK Y J, CHANG K N. Individual and group behavior-based customer profile model for personalized product recommendation [J]. Expert Syst with App, 2009, 36(2): 1932–1939.

    Article  MathSciNet  Google Scholar 

  20. LIN P, YANG F, YU X, XU Q. Personalized e-commerce recommendation based on ontology [C]// Proc of Int Conf on Internet Computing in Science and Engineering. Piscataway, USA: IEEE, 2008: 201–206.

    Google Scholar 

  21. ZHANG J, WANG Y, VASSILEVA J. SocConnect: A personalized social network aggregator and recommender [C]// Info Proc & Manage. Tarrytown, USA: Pergamon Press Inc 2013, 49(3): 721–737.

    Article  Google Scholar 

  22. FERNANDEZ-LUNA J M, HUETE J F, CASTELLS P. Personalization and recommendation in information access [J]. Info Proc & Manage, 2013, 49(3): 637–639.

    Article  Google Scholar 

  23. MARUNG U. A novel clustering method and its application to E-commerce recommendation systems [D]. Chiang Mai: Chiang Mai University, 2013.

    Google Scholar 

  24. KRASNOGOR N, SMITH J. A tutorial for competent memetic algorithms: model, taxonomy, and design issues [J]. IEEE Trans on Evol Computation, 2005, 9(5): 474–488.

    Article  Google Scholar 

  25. NERI F, COTTA C, MOSCATO P. Handbook of Memetic Algorithms [M]. Berlin: Springer, 2012: 1–370.

    Book  Google Scholar 

  26. YUSTA S C. Different metaheuristic strategies to solve the feature selection problem [J]. Pattern Recognition Letters, 2009, 30(5): 525–534.

    Article  Google Scholar 

  27. GUIMARAES F G, CAMPELO F, IGARASHI H, LOWTHER D A, RAMIREZ J A. Optimization of cost functions using evolutionary algorithms with local learning and local search [J]. IEEE Trans on Magnetics, 2007, 43(4): 1641–1644.

    Article  Google Scholar 

  28. HRNCIC D, MERNIK M, BRYANT B R. Improving grammar inference by a memetic algorithm [J]. IEEE Trans on Syst Man, and Cybernetics, 2012, 42(5): 692–703.

    Article  Google Scholar 

  29. WANG Z, SUN X. An efficient web query optimization algorithm based on LDA and MA [C]// Proc of Int Conf on Multimedia and Inform Tech. Piscataway, USA: IEEE, 2008: 50–53.

    Google Scholar 

  30. NERI F, MININNO E. Memetic compact differential evolution for cartesian robot control [J]. IEEE Mag on Computational Intell, 2010, 5(2): 54–65.

    Article  Google Scholar 

  31. AYDEMIR M E, GUNEL T, KARGIN S, ERER I, KURNAZ S. SAR image processing by a memetic algorithm [C]// Proc of Int Conf on Recent Advances in Space Technologies. Piscataway, USA: IEEE, 2005: 684–687.

    Google Scholar 

  32. BANATI H, MEHTA S. Memetic collaborative filtering based recommender system [C]// Proc of Int Conf on Inf Tech for Real World Problems. Washington DC, USA: IEEE Computer Society, 2010: 102–107.

    Google Scholar 

  33. ANG J H, TAN K C, MAMUN A A. An evolutionary memetic algorithm for rule extraction [J]. Expert Syst With App, 2010, 37(2): 1302–1315.

    Article  Google Scholar 

  34. GOLDBERG D E. Genetic algorithms in search, optimization, and machine learning [M]. Boston, USA: Addison-Wesley Publishing Company, Inc., 1989: 1–432.

    Google Scholar 

  35. KIM K J, AHN H. A recommender system using GA k-means clustering in an online shopping market [J]. Expert Syst With App, 2008, 34(2): 1200–1209.

    Article  Google Scholar 

  36. JAIN A K, MURTY M N, FLYNN P J. Data clustering: A review [J]. ACM Computing Surveys, 1999, 31(3): 265–323.

    Article  Google Scholar 

  37. JAIN A K. Data clustering: 50 years beyond k-means [J]. Pattern Recognition Letters, 2010, 31(8): 651–666.

    Article  Google Scholar 

  38. MARUNG U, THEERA-UMPON N, AUEPHANWIRIYAKUL S. Visual clustering method using genetic algorithm and image manipulation [C]// Proc of IEEE Int Symp on Intell Signal Process and Commun Syst. Piscataway, USA: IEEE, 2011: 1–5.

    Google Scholar 

  39. DUNN J C. A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters [J]. Journal of Cybernetics, 1973, 3(3): 32–57.

    Article  MATH  MathSciNet  Google Scholar 

  40. KARYPIS G. Evaluation of item-based top-N recommendation algorithms [C]// Proc of Int Conf Inf and Knowledge Manage. New York, USA: ACM, 2001: 247–254.

    Google Scholar 

  41. HERLOCKER J L, KONSTAN J A, TERVEEN L G, RIEDL J T. Evaluating collaborative filtering recommender systems [J]. ACM Trans on Inf Syst, 2004, 22(1): 5–53.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand

    Ukrit Marung & Nipon Theera-Umpon

  2. Department of Computer Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, 50200, Thailand

    Sansanee Auephanwiriyakul

Authors
  1. Ukrit Marung
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  2. Nipon Theera-Umpon
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  3. Sansanee Auephanwiriyakul
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Correspondence to Nipon Theera-Umpon.

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Marung, U., Theera-Umpon, N. & Auephanwiriyakul, S. Applying memetic algorithm-based clustering to recommender system with high sparsity problem. J. Cent. South Univ. 21, 3541–3550 (2014). https://doi.org/10.1007/s11771-014-2334-4

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  • DOI: https://doi.org/10.1007/s11771-014-2334-4

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