DOI QR코드

DOI QR Code

A Feasibility Study on Adopting Individual Information Cognitive Processing as Criteria of Categorization on Apple iTunes Store

  • Zhang, Chao (College of Business, Hankuk University of Foreign Studies) ;
  • Wan, Lili (College of Business, Hankuk University of Foreign Studies)
  • Received : 2018.05.26
  • Accepted : 2018.06.28
  • Published : 2018.06.30

Abstract

Purpose More than 7.6 million mobile apps could be approved on both Apple iTunes Store and Google Play. For managing those existed Apps, Apple Inc. established twenty-four primary categories, as well as Google Play had thirty-three primary categories. However, all of their categorizations have appeared more and more problems in managing and classifying numerous apps, such as app miscategorized, cross-attribution problems, lack of categorization keywords index, etc. The purpose of this study focused on introducing individual information cognitive processing as the classification criteria to update the current categorization on Apple iTunes Store. Meanwhile, we tried to observe the effectiveness of the new criteria from a classification process on Apple iTunes Store. Design/Methodology/Approach A research approach with four research stages were performed and a series of mixed methods was developed to identify the feasibility of adopting individual information cognitive processing as categorization criteria. By using machine-learning techniques with Term Frequency-Inverse Document Frequency and Singular Value Decomposition, keyword lists were extracted. By using the prior research results related to car app's categorization, we developed individual information cognitive processing. Further keywords extracting process from the extracted keyword lists was performed. Findings By TF-IDF and SVD, keyword lists from more than five thousand apps were extracted. Furthermore, we developed individual information cognitive processing that included a categorization teaching process and learning process. Three top three keywords for each category were extracted. By comparing the extracted results with prior studies, the inter-rater reliability for two different methods shows significant reliable, which proved the individual information cognitive processing to be reliable as criteria of categorization on Apple iTunes Store. The updating suggestions for Apple iTunes Store were discussed in this paper and the results of this paper may be useful for app store hosts to improve the current categorizations on app stores as well as increasing the efficiency of app discovering and locating process for both app developers and users.

Keywords

References

  1. Albarracin, D., Johnson, B. T., and Zanna, M. P., "The handbook of attitudes", Mahwah, N.J: Lawrence Erlbaum Associates Publishers, 2005.
  2. Alessandra Gorla., Gross, F. and Zeller, A., "Checking app behavior against app descriptions," in Proc. Int. Conf. Softw. Eng., 2014, pp. 1025-1035.
  3. Atkinson, R. C., and Shiffrin, R. M., "Human memory: A proposed system and its control processes", Psychology of learning and motivation, 2, 1968, pp. 89-195.
  4. Baeza-Yates, R., and Ribeiro-Neto, B., "Modern information retrieval", Packt Publishing Ltd, 1999, vol. 9.
  5. Choedon, T. and Lee, Y., "Classification and Evaluation of Service Requirements in Mobile Tourism Application Using Kano Model and AHP", The Journal of Information Systems, 27 (1), 2018, pp. 43-65. https://doi.org/10.5859/KAIS.2018.27.1.43
  6. Craik, F. I., and Lockhart, R. S., "Levels of processing: A framework for memory research", Journal of verbal learning and verbal behavior, 11(6), 1972, pp. 671-684. https://doi.org/10.1016/S0022-5371(72)80001-X
  7. David Kreyenhagen, C., Aleshin, T. I., Bouchard, J. E., Wise, A. M. I., and Zalegowski, R. K., "Using supervised learning to classify clothing brand styles," in 2014 Systems and Information Engineering Design Symposium (SIEDS). Charlottesville, VA, USA: IEEE, apr 2014, pp.239-243.
  8. Eck, M., Vogel, S., and Waibel, A., "Low Cost Portability for Statistical Machine Translation based on N-gram Frequency and TF-IDF," in International Workshop on Spoken Language Translation, IWSLT 2005, Pittsburgh, PA, USA, 2005, pp. 61-67.
  9. Freitag, D., "Machine learning for information extraction in informal domains," Machine learning, vol. 39, no. 2-3, pp. 169-202, 2000. https://doi.org/10.1023/A:1007601113994
  10. Giacomo Berardi, A. E., Fagni, T., and Sebastiani, F., "Multi-store metadatabased supervised mobile app classification," in Proc. 30th Annu. ACM Symp. Appl. Comput., 2015, pp. 585-588.
  11. Guo, Q., "An Effective Algorithm for Improving the Performance of Naive Bayes for Text Classification," in 2010 Second International Conference on Computer Research and Development, no. 1. Kuala Lumpur, Malaysia: IEEE, 2010, pp. 699-701.
  12. Hong, T., Niu, H., Im, G., and Park, J., "Multi-Topic Sentiment Analysis Using LDA for Online Review", The Journal of Information Systems, 27(1), 2018, pp. 89-110. https://doi.org/10.5859/KAIS.2018.27.1.89
  13. Islam, M.R., "Numeric rating of Apps on Google Play Store by sentiment analysis on user reviews," in 2014 International Conference on Electrical Engineering and Information & Communication Technology. Dhaka, Bangladesh: IEEE, Apr 2014, pp. 1-4.
  14. Katherine, M., "Theories of message processing", Chapter 8, Communication theories: perspectives, processes, and contexts. McGraw-Hill, 2005, pp. 129.
  15. Kruglanski, A. W., Van Lange, P. A. M., and Higgins, E. T., "Handbook of theories of social psychology", London, England: Sage. 2011, pp. 224-245.
  16. Lulu, L. B., and Kuflik, T., "Wise mobile icons organization: Apps taxonomy classification using functionality mining to ease apps finding," Mobile Inf. Syst., vol. 2016, 2016, Art. no. 3083450.
  17. Maalej, W., and Nabil, H., "Bug report, feature request, or simply praise? On automatically classifying app reviews," in 2015 IEEE 23rd International Requirements Engineering Conference (RE). Ottawa, ON: IEEE, aug 2015, pp. 116-125.
  18. McMillan, C., Vasquez, M. L., Poshyvanyk, D., and Grechanik, M., "Categorizing Software Applications for Maintenance", 27th IEEE International Conference on Software Maintenance (ICSM), 2011.
  19. Miller, G. A., "The cognitive revolution: a historical perspective", Trends in Cognitive Sciences, 7, 1956, pp. 141-145.
  20. Miller, G. A., "The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review", 63, 1956, pp. 81-97. https://doi.org/10.1037/h0043158
  21. Morris, C. D., Bransford, J. D., & Franks, J. J., "Levels of processing versus transfer appropriate processing", Journal of verbal learning and verbal behavior, 16(5), 1977, pp. 519-533. https://doi.org/10.1016/S0022-5371(77)80016-9
  22. Nithya, R., and Maheswari, D., "Sentiment Analysis on Unstructured Review," in 2014 International Conference on Intelligent Computing Applications. Coimbatore, India: IEEE, mar 2014, pp. 367-371.
  23. Olabenjo, B., "Applying Naïve Bayes Classification to Google Play Apps Categorization", arXiv:1608.08574v1 [cs.LG], 30 Aug 2016.
  24. Petty, R. E., and Cacioppo, J. T., "Source factors and the elaboration likelihood model of persuasion", Advances in Consumer Research. 11, 1984, pp. 668-672. https://doi.org/10.1086/209003
  25. Petty, R. E., and Cacioppo, J. T., "The elaboration likelihood model of persuasion". Advances in Experimental Social Psychology: 129, 1986, doi:10.1016/s0065-2601(08)60214-2.
  26. Petty, R.E., "Communication and persuasion: central and peripheral routes to attitude change." Springer-Verlag, New York, 1986.
  27. Petty, R.E., and Cacioppo, J.T., "Communication and Persuasion: Central and Peripheral Routes to Attitude Change", New York; Springer-Verlag, 1986.
  28. Schnack, H.G., Nieuwenhuis, M., Van Haren, N.E., Abramovic, L., Scheewe, T.W., Brouwer, R.M., Hulshoff Pol, H.E., and Kahn, R.S., "Canstructural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects," NeuroImage, vol. 84, 2014, pp. 299-306. https://doi.org/10.1016/j.neuroimage.2013.08.053
  29. Shlomo, A., "An information processing theory of family dysfunction". Psychotherapy: Theory, Research, Practice, Training. 24, 1987, pp. 477-495. https://doi.org/10.1037/h0085745
  30. Smeeton, N.C., "Early History of the Kappa Statistic", Biometrics, 1985, 41: 795, JSTOR 2531300.
  31. Surian, D., Seneviratne, S., Seneviratne, A., and Chawla, S., "App Miscategorization Detection: A Case Study on Google Play", IEEE Transactions on Knowledge and Data Engineering, 29 (8), 2017, pp. 1591-1604. https://doi.org/10.1109/TKDE.2017.2686851
  32. Xu, W., Li, C., and Jiang, X., "The application of theory of planned behaviors (TPB) in volunteers behaviors", Human Resource Management, (11), 2012, 102.
  33. Zhang C., and Wan, L., "Evaluation and Functionality Stems Extraction for App Categorization on Apple iTunes Store by Using Mixed Methods: Data Mining for Categorization Improvement", Journal of Information Technology Services, 17 (2), 2018, pp. 111-128. https://doi.org/10.9716/KITS.2018.17.2.111
  34. Zhang C., Wan, L., and Min, D., "A Classification of Car-related Mobile Apps: For App Development from a Convergence Perspective", Journal of Digital Convergence, Vol. 15(3), 2017, pp. 77-86. https://doi.org/10.14400/JDC.2017.15.3.77
  35. Zhang C., Wan, L., and Min, D., "Car App's Persuasive Design Principles and Behavior Change", Proceedings of the International Conference on Internet Technologies and Society 2016, ITS 2016 Melbourne, Australia, Dec 2016, pp. 73-82.
  36. Zhang C., Wan, L., and Min, D., "Persuasive Design Principles of Car Apps", Proceedings of Business Information Systems, 19TH International Conference in BIS 2016, Leipzig, Germany, July 2016, pp. 397-410.
  37. Zhu, H., Chen, E., Xiong, H., Cao, H., and Tian, J., "Exploiting enriched contextual information for mobile app classification," in Proceedings of 21st ACM International Conference in Information Knowledge Management, 2012, pp. 1617-1621.
  38. Zhu, H., Chen, E., Xiong, H., Cao, H., and Tian, J., "Mobile app classification with enriched contextual information," IEEE Trans. Mobile Comput., 13(7), 2014, pp. 1550-1563. https://doi.org/10.1109/TMC.2013.113