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Hybrid bio-inspired user clustering for the generation of diversified recommendations

  • R. Logesh
  • V. SubramaniyaswamyEmail author
  • V. Vijayakumar
  • Xiao-Zhi Gao
  • Gai-Ge Wang
Original Article
  • 19 Downloads

Abstract

The research and development of recommender systems are traditionally focused on the enhancement and guaranteeing the recommendation accuracy to achieve user satisfaction. On the other hand, the alternative recommendation qualities such as diversity and novelty have received significant attention from researchers in recent times. In this paper, we present a detailed study of the diversity in recommender systems to help researchers in the development of recommendation approaches to generate efficient recommendations. We have also analyzed the existing works for assessment of impact and quality of diversified recommendations. Based on our detailed investigation of the diversity in recommendations, we shift the generic focus from accuracy objectives to explore beyond the accuracy of recommendations. The need for recommender systems producing diversified recommendations without compromising the accuracy is very high to meet the growing demands of users. To address the personalization problem in travel recommender systems, we present the hybrid swarm intelligence clustering ensemble-based recommendation framework to generate diverse and accurate Point of Interest recommendations. Our proposed recommendation approach employs multiple swarm optimization algorithms to frame a clustering ensemble for the generation of efficient user clustering. We have evaluated our proposed recommendation approach over a real-time large-scale dataset of TripAdvisor to estimate the quality of recommendations in terms of diversity and accuracy. The experimental results demonstrate the enhanced efficiency of the proposed recommendation approach over state-of-the-art techniques.

Keywords

Recommender system Personalization Diversity Accuracy Clustering Swarm intelligence 

Notes

Acknowledgements

The authors are grateful to the Science and Engineering Research Board (SERB), Department of Science and Technology, New Delhi, for the financial support (No. YSS/2014/000718/ES). Authors also thank SASTRA Deemed University, Thanjavur, for providing the infrastructural facilities to carry out this research work.

References

  1. 1.
    Abbassi Z, Amer-Yahia S, Lakshmanan LV, Vassilvitskii S, Yu C (2009) Getting recommender systems to think outside the box. In: Proceedings of the third ACM conference on recommender systems. ACM, pp 285–288Google Scholar
  2. 2.
    Abbassi Z, Mirrokni VS, Thakur M (2013) Diversity maximization under matroid constraints. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 32–40Google Scholar
  3. 3.
    Adamopoulos P, Tuzhilin A (2015) On unexpectedness in recommender systems: or how to better expect the unexpected. ACM Trans Intell Syst Technol (TIST) 5(4):54Google Scholar
  4. 4.
    Adomavicius G, Kwon Y (2011) Maximizing aggregate recommendation diversity: a graph-theoretic approach. In: Proceedings of the 1st international workshop on novelty and diversity in recommender systems (DiveRS 2011), pp 3–10Google Scholar
  5. 5.
    Adomavicius G, Kwon Y (2012) Improving aggregate recommendation diversity using ranking-based techniques. IEEE Trans Knowl Data Eng 24(5):896–911Google Scholar
  6. 6.
    Adomavicius G, Kwon Y (2014) Optimization-based approaches for maximizing aggregate recommendation diversity. INFORMS J Comput 26(2):351–369MathSciNetzbMATHGoogle Scholar
  7. 7.
    Agrawal R, Gollapudi S, Halverson A, Ieong S (2009) Diversifying search results. In: Proceedings of the second ACM international conference on web search and data mining. ACM, pp 5–14Google Scholar
  8. 8.
    Alam S, Dobbie G, Koh YS, Riddle P, Rehman SU (2014) Research on particle swarm optimization based clustering: a systematic review of literature and techniques. Swarm Evolut Comput 17:1–13Google Scholar
  9. 9.
    An J, Kang Q, Wang L, Wu Q (2013) Mussels wandering optimization: an ecologically inspired algorithm for global optimization. Cognit Comput 5(2):188–199Google Scholar
  10. 10.
    André P, Teevan J, Dumais ST (2009) Discovery is never by chance: designing for (un) serendipity. In: Proceedings of the seventh ACM conference on creativity and cognition. ACM, pp 305–314Google Scholar
  11. 11.
    Assent I (2012) Clustering high dimensional data. Wiley Interdisc Rev Data Min Knowl Discovery 2(4):340–350Google Scholar
  12. 12.
    Aytekin T, Karakaya MÖ (2014) Clustering-based diversity improvement in top-N recommendation. J Intell Inf Syst 42(1):1–18Google Scholar
  13. 13.
    Baeza-Yates R, Ribeiro-Neto B (1999) Modern information retrieval, vol 463. ACM press, New YorkGoogle Scholar
  14. 14.
    Barry Crabtree I, Soltysiak SJ (1998) Identifying and tracking changing interests. Int J Digit Libr 2(1):38–53Google Scholar
  15. 15.
    Basile P, Musto C, de Gemmis M, Lops P, Narducci F, Semeraro G (2014) Aggregation strategies for linked open data-enabled recommender systems. In: European semantic web conferenceGoogle Scholar
  16. 16.
    Bedi P, Agarwa S, Singhal A, Jain E, Gupta G (2015) A novel semantic clustering approach for reasonable diversity in news recommendations. In: Computational intelligence in data mining, vol 1. Springer, pp 437–445Google Scholar
  17. 17.
    Bedi P, Sharma R (2012) Trust based recommender system using ant colony for trust computation. Expert Syst Appl 39(1):1183–1190Google Scholar
  18. 18.
    Bezdek JC, Ehrlich R, Full W (1984) FCM: the fuzzy c-means clustering algorithm. Comput Geosci 10(2–3):191–203Google Scholar
  19. 19.
    Bezerra B, de Carvalho FDA, Ramalho GL, Zucker JD (2002) Speeding up recommender systems with meta-prototypes. In: Brazilian symposium on artificial intelligence. Springer, Berlin, pp 227–236Google Scholar
  20. 20.
    Boim R, Milo T, Novgorodov S (2011) Diversification and refinement in collaborative filtering recommender. In: Proceedings of the 20th ACM international conference on Information and knowledge management. ACM, pp 739–744Google Scholar
  21. 21.
    Bradley K, Smyth B (2001) Improving recommendation diversity. In: Proceedings of the twelfth Irish conference on artificial intelligence and cognitive science, Maynooth, Ireland, pp 85–94Google Scholar
  22. 22.
    Bridge D, Kelly JP (2006) Ways of computing diverse collaborative recommendations. In: International conference on adaptive hypermedia and adaptive web-based systems. Springer, Berlin, pp 41–50Google Scholar
  23. 23.
    Buczak A, Zimmerman J, Kurapati K (2002) Personalization: improving ease-of-use, trust and accuracy of a TV show recommender. http://pages.stern.nyu.edu/~ksk227/TV02_Ease_of_Use_Trust_Accuracy.pdf
  24. 24.
    Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User Adapt Interact 12(4):331–370zbMATHGoogle Scholar
  25. 25.
    Carbonell J, Goldstein J (1998) The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 335–336Google Scholar
  26. 26.
    Castagnos S, Brun A, Boyer A (2013) When diversity is needed… But not expected! In: International conference on advances in information mining and management. IARIA XPS Press, pp 44–50Google Scholar
  27. 27.
    Castells P, Vargas S, Wang J (2011) Novelty and diversity metrics for recommender systems: choice, discovery and relevance. In: International workshop diversity document retrieval (DDR 2011) 33rd European conference on information retrieval (ECIR 2011), Dublin, Ireland, pp 29–36Google Scholar
  28. 28.
    Celma Ò (2009) Music recommendation and discovery in the long tail. PhD dissertation. Universitat Pompeu FabraGoogle Scholar
  29. 29.
    Chen S, Xu Z, Tang Y (2014) A hybrid clustering algorithm based on fuzzy c-means and improved particle swarm optimization. Arab J Sci Eng 39(12):8875–8887MathSciNetzbMATHGoogle Scholar
  30. 30.
    Cheng LC, Wang HA (2014) A fuzzy recommender system based on the integration of subjective preferences and objective information. Appl Soft Comput 18:290–301Google Scholar
  31. 31.
    Choi SM, Han YS (2010) A content recommendation system based on category correlations. In: 2010 Fifth international multi-conference on computing in the global information technology (ICCGI). IEEE, pp 66–70Google Scholar
  32. 32.
    Clarke CL, Kolla M, Cormack GV, Vechtomova O, Ashkan A, Büttcher S, MacKinnon I (2008) Novelty and diversity in information retrieval evaluation. In: Proceedings of the 31st annual international ACM SIGIR conference on research and development in information retrieval, pp 659–666. ACMGoogle Scholar
  33. 33.
    Di Noia T, Ostuni VC, Rosati J, Tomeo P, Di Sciascio E (2014) An analysis of users’ propensity toward diversity in recommendations. In: Proceedings of the 8th ACM conference on recommender systems. ACM, pp 285–288Google Scholar
  34. 34.
    Domeniconi C, Al-Razgan M (2009) Weighted cluster ensembles: methods and analysis. ACM Trans Knowl Discov Data (TKDD) 2(4):17Google Scholar
  35. 35.
    Duan H, Li C (2015) Quantum-behaved brain storm optimization approach to solving Loney’s solenoid problem. IEEE Trans Magn 51(1):1–7Google Scholar
  36. 36.
    Ekstrand MD, Harper FM, Willemsen MC, Konstan JA (2014) User perception of differences in recommender algorithms. In: Proceedings of the 8th ACM conference on recommender systems. ACM, pp 161–168Google Scholar
  37. 37.
    Fan XP, Xie YS, Liao ZF, Li XQ, Liu LM (2011) A weighted cluster ensemble algorithm based on graph. In: 2011 IEEE 10th international conference on trust, security and privacy in computing and communications (TrustCom). IEEE, pp 1519–1523Google Scholar
  38. 38.
    Feng Y, Wang GG, Deb S, Lu M, Zhao XJ (2017) Solving 0–1 knapsack problem by a novel binary monarch butterfly optimization. Neural Comput Appl 28(7):1619–1634Google Scholar
  39. 39.
    Feng Y, Wang GG, Li W, Li N (2017) Multi-strategy monarch butterfly optimization algorithm for discounted {0–1} knapsack problem. Neural Comput Appl 30:3019–3036.  https://doi.org/10.1007/s00521-017-2903-1 Google Scholar
  40. 40.
    Fleder DM, Hosanagar K (2007) Recommender systems and their impact on sales diversity. In: Proceedings of the 8th ACM conference on electronic commerce. ACM, pp 192–199Google Scholar
  41. 41.
    Forestiero A (2015) AIRS: ant-inspired recommendation system. In: Intelligent Systems' 2014. Springer International Publishing, pp 213–224Google Scholar
  42. 42.
    Frolov E, Oseledets I (2017) Tensor methods and recommender systems. Wiley Interdisc Rev Data Min Knowl Discovery 7(3). https://doi.org/10.1002/widm.1201
  43. 43.
    Ge M, Delgado-Battenfeld C, Jannach D (2010) Beyond accuracy: evaluating recommender systems by coverage and serendipity. In: Proceedings of the fourth ACM conference on recommender systems. ACM, pp 257–260Google Scholar
  44. 44.
    Ge M, Gedikli F, Jannach D (2011) Placing high-diversity items in top-n recommendation lists. In: Proceedings of the 9th workshop on intelligent techniques for web personalization and recommender systems (ITWP 2011), Barcelona, SpainGoogle Scholar
  45. 45.
    Good N, Schafer JB, Konstan JA, Borchers A, Sarwar B, Herlocker J, Riedl J (1999) Combining collaborative filtering with personal agents for better recommendations. In: AAAI/IAAI, pp 439–446Google Scholar
  46. 46.
    Gu W, Dong S, Chen M (2016) Personalized news recommendation based on articles chain building. Neural Comput Appl 27(5):1263–1272Google Scholar
  47. 47.
    Hall LO (2012) Objective function-based clustering. Wiley Interdisc Rev Data Min Knowl Discovery 2(4):326–339Google Scholar
  48. 48.
    Hand DJ, Mannila H, Smyth P (2001) Principles of data mining. MIT Press, CambridgeGoogle Scholar
  49. 49.
    Herlocker JL, Konstan JA, Terveen LG, Riedl JT (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst (TOIS) 22(1):5–53Google Scholar
  50. 50.
    Ho YC, Chiang YT, Hsu JYJ (2014) Who likes it more? Mining worth-recommending items from long tails by modeling relative preference. In: Proceedings of the 7th ACM international conference on web search and data mining. ACM, pp 253–262Google Scholar
  51. 51.
    Holland H (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan, Ann ArborzbMATHGoogle Scholar
  52. 52.
    Hu R, Pu P (2011) Helping users perceive recommendation diversity. In: DiveRS@ RecSys, pp 43–50Google Scholar
  53. 53.
    Huang Z, Chen H, Zeng D (2004) Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Trans Inf Syst (TOIS) 22(1):116–142Google Scholar
  54. 54.
    Hunt JE, Cooke DE (1996) Learning using an artificial immune system. J Netw Comput Appl 19(2):189–212Google Scholar
  55. 55.
    Hurley N, Zhang M (2011) Novelty and diversity in top-n recommendation—analysis and evaluation. ACM Trans Internet Technol (TOIT) 10(4):14Google Scholar
  56. 56.
    Indragandhi V, Logesh R, Subramaniyaswamy V, Vijayakumar V, Siarry P, Uden L (2018) Multi-objective optimization and energy management in renewable based AC/DC microgrid. Comput Electr Eng 70:179–198Google Scholar
  57. 57.
    Ishikawa M, Geczy P, Izumi N, Yamaguchi T (2008) Long tail recommender utilizing information diffusion theory. In: Proceedings of the 2008 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, vol 01, pp 785–788. IEEE Computer SocietyGoogle Scholar
  58. 58.
    Izakian H, Abraham A (2011) Fuzzy C-means and fuzzy swarm for fuzzy clustering problem. Expert Syst Appl 38(3):1835–1838Google Scholar
  59. 59.
    Javari A, Jalili M (2015) A probabilistic model to resolve diversity–accuracy challenge of recommendation systems. Knowl Inf Syst 44(3):609–627Google Scholar
  60. 60.
    Jia J, Xiao X, Liu B (2012) Similarity-based spectral clustering ensemble selection. In: 2012 9th International conference on fuzzy systems and knowledge discovery (FSKD). IEEE, pp 1071–1074Google Scholar
  61. 61.
    Jiang H, Qi X, Sun H (2014) Choice-based recommender systems: a unified approach to achieving relevancy and diversity. Oper Res 62(5):973–993MathSciNetzbMATHGoogle Scholar
  62. 62.
    Katarya R, Verma OP (2016) Recommender system with grey wolf optimizer and FCM. Neural Comput Appl 30:1679–1687.  https://doi.org/10.1007/s00521-016-2817-3 Google Scholar
  63. 63.
    Kaminskas M, Bridge D (2016) Diversity, serendipity, novelty, and coverage: a survey and empirical analysis of beyond-accuracy objectives in recommender systems. ACM Trans Interact Intell Syst (TiiS) 7(1):2Google Scholar
  64. 64.
    Kang Q, Liu S, Zhou M, Li S (2016) A weight-incorporated similarity-based clustering ensemble method based on swarm intelligence. Knowl Based Syst 104:156–164Google Scholar
  65. 65.
    Kapoor K, Kumar V, Terveen L, Konstan JA, Schrater P (2015) I like to explore sometimes: adapting to dynamic user novelty preferences. In: Proceedings of the 9th ACM conference on recommender systems. ACM, pp 19–26Google Scholar
  66. 66.
    Koohi H, Kiani K (2016) User based collaborative filtering using fuzzy C-means. Measurement 91:134–139Google Scholar
  67. 67.
    Kunaver M, Požrl T (2017) Diversity in recommender systems—a survey. Knowl Based Syst 123:154–162Google Scholar
  68. 68.
    Lathia N, Hailes S, Capra L, Amatriain X (2010) Temporal diversity in recommender systems. In: Proceedings of the 33rd international ACM SIGIR conference on research and development in information retrieval. ACM, pp 210–217Google Scholar
  69. 69.
    Lee K, Lee K (2015) Escaping your comfort zone: a graph-based recommender system for finding novel recommendations among relevant items. Expert Syst Appl 42(10):4851–4858Google Scholar
  70. 70.
    L’Huillier A, Castagnos S, Boyer A (2014) Understanding usages by modeling diversity over time. In: 22nd Conference on user modeling, adaptation, and personalization, vol 1181Google Scholar
  71. 71.
    Li C, Zhou J, Kou P, Xiao J (2012) A novel chaotic particle swarm optimization based fuzzy clustering algorithm. Neurocomputing 83:98–109Google Scholar
  72. 72.
    Li F, Xu G, Cao L (2016) Two-level matrix factorization for recommender systems. Neural Comput Appl 27(8):2267–2278Google Scholar
  73. 73.
    Liu JG, Shi K, Guo Q (2012) Solving the accuracy–diversity dilemma via directed random walks. Phys Rev E 85(1):016118Google Scholar
  74. 74.
    Logesh R, Subramaniyaswamy V (2017) Learning recency and inferring associations in location based social network for emotion induced point-of-interest recommendation. J Inf Sci Eng 33(6):1629–1647Google Scholar
  75. 75.
    Logesh R, Subramaniyaswamy V, Vijayakumar V, Gao XZ, Indragandhi V (2018) A hybrid quantum-induced swarm intelligence clustering for the urban trip recommendation in smart city. Future Gener Comput Syst 83:653–673Google Scholar
  76. 76.
    Logesh R, Subramaniyaswamy V, Vijayakumar V, Li X (2018) Efficient user profiling based intelligent travel recommender system for individual and group of users. Mob Netw Appl.  https://doi.org/10.1007/s11036-018-1059-2 Google Scholar
  77. 77.
    Logesh R, Subramaniyaswamy V, Vijayakumar V (2018) A personalised travel recommender system utilising social network profile and accurate GPS data. Electron Gov Int J 14(1):90–113Google Scholar
  78. 78.
    Logesh R, Subramaniyaswamy V (2017) A reliable point of interest recommendation based on trust relevancy between users. Wirel Pers Commun 97(2):2751–2780Google Scholar
  79. 79.
    Logesh R, Subramaniyaswamy V, Malathi D, Senthilselvan N, Sasikumar A, Saravanan P (2017) Dynamic particle swarm optimization for personalized recommender system based on electroencephalography feedback. Biomed Res 28(13):5646–5650Google Scholar
  80. 80.
    Logesh R, Subramaniyaswamy V (2019) Exploring hybrid recommender systems for personalized travel applications. In: Cognitive informatics and soft computing. Springer, Singapore, pp 535–544Google Scholar
  81. 81.
    Malone TW, Grant KR, Turbak FA, Brobst SA, Cohen MD (1987) Intelligent information-sharing systems. Commun ACM 30(5):390–402Google Scholar
  82. 82.
    Markowitz H (1952) Portfolio selection. J Finance 7(1):77–91Google Scholar
  83. 83.
    McNee SM, Riedl J, Konstan JA (2006) Being accurate is not enough: how accuracy metrics have hurt recommender systems. In: CHI’06 extended abstracts on human factors in computing systems. ACM, pp 1097–1101Google Scholar
  84. 84.
    Mirkovic J, Cvetkovic D, Tomca N, Cveticanin S, Slijepcevic S, Obradovic V et al (1999) Genetic algorithms for intelligent internet search: a survey and a package for experimenting with various locality types. IEEE TCCA Newsl 118–119Google Scholar
  85. 85.
    Mladenic D (1999) Text-learning and related intelligent agents: a survey. IEEE Intell Syst Appl 14(4):44–54Google Scholar
  86. 86.
    Mourão F, Fonseca C, Araujo CS, Meira W Jr (2011) The oblivion problem: exploiting forgotten items to improve recommendation diversity. In: DiveRS@ RecSys, pp 27–34Google Scholar
  87. 87.
    Nakatsuji M, Fujiwara Y, Tanaka A, Uchiyama T, Fujimura K, Ishida T (2010) Classical music for rock fans? Novel recommendations for expanding user interests. In: Proceedings of the 19th ACM international conference on Information and knowledge management. ACM, pp 949–958Google Scholar
  88. 88.
    Oh J, Park S, Yu H, Song M, Park ST (2011) Novel recommendation based on personal popularity tendency. In: 2011 IEEE 11th international conference on data mining (ICDM). IEEE, pp 507–516Google Scholar
  89. 89.
    Omran MG, Salman A, Engelbrecht AP (2006) Dynamic clustering using particle swarm optimization with application in image segmentation. Pattern Anal Appl 8(4):332MathSciNetGoogle Scholar
  90. 90.
    Pal NR, Pal K, Keller JM, Bezdek JC (2005) A possibilistic fuzzy c-means clustering algorithm. IEEE Trans Fuzzy Syst 13(4):517–530Google Scholar
  91. 91.
    Park YJ, Tuzhilin A (2008) The long tail of recommender systems and how to leverage it. In: Proceedings of the 2008 ACM conference on recommender systems. ACM, pp 11–18Google Scholar
  92. 92.
    Pei Z, Hua X, Han J (2008) The clustering algorithm based on particle swarm optimization algorithm. In: 2008 International conference on intelligent computation technology and automation (ICICTA), vol 1. IEEE, pp 148–151Google Scholar
  93. 93.
    Premchaiswadi W, Poompuang P, Jongswat N, Premchaiswadi N (2013) Enhancing diversity-accuracy technique on user-based top-n recommendation algorithms. In: 2013 IEEE 37th annual computer software and applications conference workshops (COMPSACW). IEEE, pp 403–408Google Scholar
  94. 94.
    Rana S, Jasola S, Kumar R (2013) A boundary restricted adaptive particle swarm optimization for data clustering. Int J Mach Learn Cybern 4(4):391–400Google Scholar
  95. 95.
    Ravi L, Vairavasundaram S (2016) A collaborative location based travel recommendation system through enhanced rating prediction for the group of users. Comput Intell Neurosci 2016:7Google Scholar
  96. 96.
    Ren X, Lü L, Liu R, Zhang J (2014) Avoiding congestion in recommender systems. New J Phys 16(6):063057Google Scholar
  97. 97.
    Ribeiro MT, Ziviani N, Moura ESD, Hata I, Lacerda A, Veloso A (2015) Multiobjective pareto-efficient approaches for recommender systems. ACM Trans Intell Syst Technol (TIST) 5(4):53Google Scholar
  98. 98.
    Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook. Springer, New York, pp 1–35zbMATHGoogle Scholar
  99. 99.
    Salton G (1983) Introduction to modern information retrieval. McGraw-Hill, New YorkzbMATHGoogle Scholar
  100. 100.
    Sarwar BM, Karypis G, Konstan J, Riedl J (2002) Recommender systems for large-scale e-commerce: scalable neighborhood formation using clustering. In: Proceedings of the fifth international conference on computer and information technology, vol 1Google Scholar
  101. 101.
    Schein AI, Popescul A, Ungar LH, Pennock DM (2002) Methods and metrics for cold-start recommendations. In: Proceedings of the 25th annual international ACM SIGIR conference on research and development in information retrieval. ACM, pp 253–260Google Scholar
  102. 102.
    Shi Y (2011) Brain storm optimization algorithm. In: International conference in swarm intelligence. Springer, Berlin, pp 303–309Google Scholar
  103. 103.
    Shi Y, Larson M, Hanjalic A (2010) List-wise learning to rank with matrix factorization for collaborative filtering. In: Proceedings of the fourth ACM conference on recommender systems. ACM, pp 269–272Google Scholar
  104. 104.
    Shi Y, Zhao X, Wang J, Larson M, Hanjalic A (2012) Adaptive diversification of recommendation results via latent factor portfolio. In: Proceedings of the 35th international ACM SIGIR conference on research and development in information retrieval. ACM, pp 175–184Google Scholar
  105. 105.
    Silva Filho TM, Pimentel BA, Souza RM, Oliveira AL (2015) Hybrid methods for fuzzy clustering based on fuzzy c-means and improved particle swarm optimization. Expert Syst Appl 42(17):6315–6328Google Scholar
  106. 106.
    Slaney M, White W (2006) Measuring playlist diversity for recommendation systems. In: Proceedings of the 1st ACM workshop on audio and music computing multimedia. ACM, pp 77–82Google Scholar
  107. 107.
    Strehl A, Ghosh J (2002) Cluster ensembles—a knowledge reuse framework for combining multiple partitions. J Mach Learn Res 3(Dec):583–617MathSciNetzbMATHGoogle Scholar
  108. 108.
    Subramaniyaswamy V, Logesh R, Abejith M, Umasankar S, Umamakeswari A (2017) Sentiment analysis of tweets for estimating criticality and security of events. J Organ End User Comput (JOEUC) 29(4):51–71Google Scholar
  109. 109.
    Subramaniyaswamy V, Logesh R, Chandrashekhar M, Challa A, Vijayakumar V (2017) A personalised movie recommendation system based on collaborative filtering. Int J High Perform Comput Netw 10(1–2):54–63Google Scholar
  110. 110.
    Subramaniyaswamy V, Logesh R (2017) Adaptive KNN based recommender system through mining of user preferences. WirelPers Commun 97(2):2229–2247Google Scholar
  111. 111.
    Subramaniyaswamy V, Manogaran G, Logesh R, Vijayakumar V, Chilamkurti N, Malathi D, Senthilselvan N (2018) An ontology-driven personalized food recommendation in IoT-based healthcare system. J Supercomput.  https://doi.org/10.1007/s11227-018-2331-8 Google Scholar
  112. 112.
    Subramaniyaswamy V, Logesh R, Indragandhi V (2018) Intelligent sports commentary recommendation system for individual cricket players. Int J Adv Intell Paradig 10(1–2):103–117Google Scholar
  113. 113.
    Sumathi G, Sendhilkumar S, Mahalakshmi GS (2016) Hybrid recommendation system using particle swarm optimization and user access based ranking. In: Proceedings of the international conference on informatics and analytics. ACM, p 68Google Scholar
  114. 114.
    Sun J, Feng B, Xu W (2004) Particle swarm optimization with particles having quantum behavior. In: Congress on evolutionary computation, 2004. CEC2004, vol 1. IEEE, pp 325–331Google Scholar
  115. 115.
    Sun J, Xu W, Feng B (2004) A global search strategy of quantum-behaved particle swarm optimization. In: 2004 IEEE conference on cybernetics and intelligent systems, vol 1. IEEE, pp 111–116Google Scholar
  116. 116.
    Tintarev N, Dennis M, Masthoff J (2013) Adapting recommendation diversity to openness to experience: a study of human behaviour. In: International conference on user modeling, adaptation, and personalization. Springer, Berlin, pp 190–202Google Scholar
  117. 117.
    Toms EG (2000) Serendipitous information retrieval. In: DELOS workshop: information seeking, searching and querying in digital libraries, pp 17–20Google Scholar
  118. 118.
    Ujjin S, Bentley PJ (2003) Particle swarm optimization recommender system. In: Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE, IEEE, pp 124–131Google Scholar
  119. 119.
    Vaishnavi S, Jayanthi A, Karthik S (2013) Ranking technique to improve diversity in recommender systems. Int J Comput Appl 68(2):20–24Google Scholar
  120. 120.
    Van Andel P (1994) Anatomy of the unsought finding. Serendipity: orgin, history, domains, traditions, appearances, patterns and programmability. Br J Philos Sci 45(2):631–648Google Scholar
  121. 121.
    Van der Merwe DW, Engelbrecht AP (2003) Data clustering using particle swarm optimization. In: The 2003 congress on evolutionary computation, 2003. CEC’03, vol 1. IEEE, pp 215–220Google Scholar
  122. 122.
    Vargas S (2011) New approaches to diversity and novelty in recommender systems. In: Fourth BCS-IRSG symposium on future directions in information access (FDIA 2011), Koblenz, vol 31Google Scholar
  123. 123.
    Vargas S (2015) Novelty and diversity enhancement and evaluation in recommender systems. Master’s thesis, Autonomous University of Madrid, Madrid, SpainGoogle Scholar
  124. 124.
    Vargas S, Castells P (2011) Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the fifth ACM conference on recommender systems. ACM, pp 109–116Google Scholar
  125. 125.
    Vargas S, Baltrunas L, Karatzoglou A, Castells P (2014) Coverage, redundancy and size-awareness in genre diversity for recommender systems. In: Proceedings of the 8th ACM conference on recommender systems. ACM, pp 209–216Google Scholar
  126. 126.
    Vairavasundaram S, Varadharajan V, Vairavasundaram I, Ravi L (2015) Data mining-based tag recommendation system: an overview. Wiley Interdiscip Rev Data Min Knowl Discov 5(3):87–112Google Scholar
  127. 127.
    Wan X, Okamoto T (2011) Utilizing learning process to improve recommender system for group learning support. Neural Comput Appl 20(5):611–621Google Scholar
  128. 128.
    Wang G, Guo L, Wang H, Duan H, Liu L, Li J (2014) Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Comput Appl 24(3–4):853–871Google Scholar
  129. 129.
    Wang GG, Gandomi AH, Alavi AH, Hao GS (2014) Hybrid krill herd algorithm with differential evolution for global numerical optimization. Neural Comput Appl 25(2):297–308Google Scholar
  130. 130.
    Wang GG, Gandomi AH, Alavi AH, Deb S (2016) A hybrid method based on krill herd and quantum-behaved particle swarm optimization. Neural Comput Appl 27(4):989–1006Google Scholar
  131. 131.
    Wang GG, Lu M, Dong YQ, Zhao XJ (2016) Self-adaptive extreme learning machine. Neural Comput Appl 27(2):291–303Google Scholar
  132. 132.
    Wang J, Zhu J (2009) Portfolio theory of information retrieval. In: Proceedings of the 32nd international ACM SIGIR conference on research and development in information retrieval. ACM, pp 115–122Google Scholar
  133. 133.
    Wilkin GA, Huang X (2007) K-means clustering algorithms: implementation and comparison. In: Second international multi-symposiums on computer and computational sciences, 2007. IMSCCS 2007. IEEE, pp 133–136Google Scholar
  134. 134.
    Xia X, Wang X, Zhou X, Zhu T (2014) Collaborative recommendation of mobile Apps: a swarm intelligence method. In: Mobile, ubiquitous, and intelligent computing. Springer, Berlin, Heidelberg, pp 405–412Google Scholar
  135. 135.
    Xu R, Wunsch D (2005) Survey of clustering algorithms. IEEE Trans Neural Netw 16(3):645–678Google Scholar
  136. 136.
    Yang Y, Li JZ (2005) Interest-based recommendation in digital library. J Comput Sci 1(1):40–46Google Scholar
  137. 137.
    Yao J, Li B (2011) Dynamic recommendation in collaborative filtering systems: a PSO based framework. In: Proceedings of the international conference on human-centric computing 2011 and embedded and multimedia computing 2011. Springer, Netherlands, pp 11–21Google Scholar
  138. 138.
    Yuan JL, Yu Y, Xiao X, Li XY (2009) SVM based classification mapping for user navigation. Int J Distrib Sens Netw 5(1):32Google Scholar
  139. 139.
    Zhang L (2013) The definition of novelty in recommendation system. J Eng Sci Technol Rev 6(3):141–145Google Scholar
  140. 140.
    Zhang C, Liang H, Wang K (2016) Trip recommendation meets real-world constraints: POI availability, diversity, and traveling time uncertainty. ACM Trans Inf Syst (TOIS) 35(1):5. https://doi.org/10.1145/2948065
  141. 141.
    Zhang L, Pedrycz W, Lu W, Liu X, Zhang L (2014) An interval weighed fuzzy c-means clustering by genetically guided alternating optimization. Expert Syst Appl 41(13):5960–5971Google Scholar
  142. 142.
    Zhang Y, Xiong X, Zhang Q (2013) An improved self-adaptive PSO algorithm with detection function for multimodal function optimization problems. Math Probl Eng 2013:1–8.  https://doi.org/10.1155/2013/716952 MathSciNetzbMATHGoogle Scholar
  143. 143.
    Zhou T, Kuscsik Z, Liu JG, Medo M, Wakeling JR, Zhang YC (2010) Solving the apparent diversity-accuracy dilemma of recommender systems. Proc Natl Acad Sci 107(10):4511–4515Google Scholar
  144. 144.
    Zhou ZH (2012) Ensemble methods: foundations and algorithms. CRC Press, Boca RatonGoogle Scholar
  145. 145.
    Ziegler CN, McNee SM, Konstan JA, Lausen G (2005) Improving recommendation lists through topic diversification. In: Proceedings of the 14th international conference on World Wide Web. ACM, pp 22–32Google Scholar
  146. 146.
    Zou DX, Deb S, Wang GG (2016). Solving IIR system identification by a variant of particle swarm optimization. Neural Comput Appl 30:685–698.  https://doi.org/10.1007/s00521-016-2338-0 Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • R. Logesh
    • 1
  • V. Subramaniyaswamy
    • 1
    Email author
  • V. Vijayakumar
    • 2
  • Xiao-Zhi Gao
    • 3
  • Gai-Ge Wang
    • 4
  1. 1.School of ComputingSASTRA Deemed UniversityThanjavurIndia
  2. 2.School of Computing Science and EngineeringVellore Institute of TechnologyChennaiIndia
  3. 3.School of ComputingUniversity of Eastern FinlandKuopioFinland
  4. 4.School of Computer Science and TechnologyJiangsu Normal UniversityXuzhouChina

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