Determining user needs through abnormality detection and heterogeneous embedding of usage sequence

  • Younghoon Lee
  • Sungzoon ChoEmail author
  • Jinhae Choi


In this study, we propose an advanced method for determining user needs based on abnormality detection and heterogeneous embedding of the usage sequences. We focus on the implied needs at the fine-grained levels based on the usage sequence, whereas previous textual review-based approaches have focused on the explicit needs at the product levels. Moreover, although previous studies regarding a usage sequence have primarily focused on an analysis of the tendency, app prediction, or recommendations, we first attempted to uncover abnormal sequences regarding user needs. Furthermore, in terms of the methodology, we then attempted a heterogeneous embedding approach to calculate the vector representation of each element of the usage sequence including the application, buttons, content, or system keys by utilizing the metapath2vec algorithm, which differs from previous studies that have focused solely on the embedding application usage. Further, to apply the abnormality detection method in determining an abnormal sequence corresponding to the user needs, we calculate the vector representation of the entire usage sequence utilizing RNN-AE based on heterogeneous embedding. After examining and evaluating the extracted abnormal sequences with the help of domain experts from LG Electronics, the experimental results verify that our proposed method can effectively extract a meaningful abnormal sequence corresponding to the implied needs. In addition, we calculated the correlation of the coefficient between the abnormality score and the importance score of the extracted sequences to compare the performance of each sequence model and the abnormality detection method.


Determining needs Implied needs Heterogeneous embedding Usage sequences Abnormality detection Sequence modeling 



We appreciate LG Electronics for providing us with the app usage dataset. Moreover, we are thankful to the domain experts involved in the examination and evaluation of the extracted usage sequences: Jungmin Park (UX designer), Minhyeok Kim (UX researcher), Christina Suh (Chief UX designer), and Shinhui Ahn (Senior UX designer), among others.


  1. 1.
    Abrahams, A. S., Jiao, J., Wang, G. A., & Fan, W. (2012). Vehicle defect discovery from social media. Decision Support Systems, 54(1), 87–97.CrossRefGoogle Scholar
  2. 2.
    Algur, S. P., & Bhat, P. (2016). Abnormal web video detection using density based lof method. International Journal of Computer Sciences and Engineering, 4, 6–14.Google Scholar
  3. 3.
    Amiriparian, S., Freitag, M., Cummins, N., & Schuller, B. (2017). Sequence to sequence autoencoders for unsupervised representation learning from audio. In Proceedings of the DCASE 2017 workshop.Google Scholar
  4. 4.
    Anzai, Y. (2012). Pattern recognition and machine learning. Amsterdam: Elsevier.Google Scholar
  5. 5.
    Baeza-Yates, R., Jiang, D., Silvestri, F., & Harrison, B. (2015). Predicting the next app that you are going to use. In Proceedings of the eighth ACM international conference on web search and data mining (pp. 285–294). ACM.Google Scholar
  6. 6.
    Bahn, S., Lee, C., Nam, C. S., & Yun, M. H. (2009). Incorporating affective customer needs for luxuriousness into product design attributes. Human Factors and Ergonomics in Manufacturing & Service Industries, 19(2), 105–127.CrossRefGoogle Scholar
  7. 7.
    Breunig, M. M., Kriegel, H. P., Ng, R. T., & Sander, J. (2000). Lof: Identifying density-based local outliers. In ACM sigmod record (Vol. 29, pp. 93–104). ACM.Google Scholar
  8. 8.
    Decker, R., & Trusov, M. (2010). Estimating aggregate consumer preferences from online product reviews. International Journal of Research in Marketing, 27(4), 293–307.CrossRefGoogle Scholar
  9. 9.
    Dong, Y., Chawla, N. V., & Swami, A. (2017). metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 135–144). ACM.Google Scholar
  10. 10.
    Fang, Y., Si, L., Somasundaram, N., & Yu, Z. (2012). Mining contrastive opinions on political texts using cross-perspective topic model. In Proceedings of the fifth ACM international conference on Web search and data mining (pp. 63–72). ACM.Google Scholar
  11. 11.
    Gabriel, A., Camargo, M., Monticolo, D., Boly, V., & Bourgault, M. (2016). Improving the idea selection process in creative workshops through contextualisation. Journal of cleaner production, 135, 1503–1513.CrossRefGoogle Scholar
  12. 12.
    Gerani, S., Carenini, G., & Ng, R. T. (2016). Modeling content and structure for abstractive review summarization. Computer Speech & Language, 53, 302–331.CrossRefGoogle Scholar
  13. 13.
    Groves, R. M. (2006). Nonresponse rates and nonresponse bias in household surveys. Public Opinion Quarterly, 70(5), 646–675.CrossRefGoogle Scholar
  14. 14.
    Gu, B., & Ye, Q. (2014). First step in social media: Measuring the influence of online management responses on customer satisfaction. Production and Operations Management, 23(4), 570–582.CrossRefGoogle Scholar
  15. 15.
    Jang, B. R., Noh, Y., Lee, S. J., & Park, S. B. (2015). A combination of temporal and general preferences for app recommendation. In 2015 International conference on big data and smart computing (BigComp) (pp. 178–185). IEEE.Google Scholar
  16. 16.
    Jang, M., Seo, S., & Kang, P. (2018). Recurrent neural network-based semantic variational autoencoder for sequence-to-sequence learning. arXiv preprint arXiv:1802.03238.
  17. 17.
    Jin, J., Ji, P., & Gu, R. (2016). Identifying comparative customer requirements from product online reviews for competitor analysis. Engineering Applications of Artificial Intelligence, 49, 61–73.CrossRefGoogle Scholar
  18. 18.
    Jin, J., Ji, P., & Kwong, C. (2016). What makes consumers unsatisfied with your products: Review analysis at a fine-grained level. Engineering Applications of Artificial Intelligence, 47, 38–48.CrossRefGoogle Scholar
  19. 19.
    Kim, H. K., Kim, H., & Cho, S. (2017). Bag-of-concepts: Comprehending document representation through clustering words in distributed representation. Neurocomputing, 266, 336–352.CrossRefGoogle Scholar
  20. 20.
    Kostakos, V., Ferreira, D., Goncalves, J., & Hosio, S. (2016). Modelling smartphone usage: A markov state transition model. In Proceedings of the 2016 ACM international joint conference on pervasive and ubiquitous computing (pp. 486–497). ACM.Google Scholar
  21. 21.
    Lappas, T., Crovella, M., & Terzi, E. (2012). Selecting a characteristic set of reviews. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 832–840). ACM.Google Scholar
  22. 22.
    Lee, C. C., & Hu, C. (2005). Analyzing hotel customers’ e-complaints from an internet complaint forum. Journal of Travel & Tourism Marketing, 17(2–3), 167–181.Google Scholar
  23. 23.
    Lee, T., & Bradlow, E. T. (2007). Automatic construction of conjoint attributes and levels from online customer reviews. University of Pennsylvania, The Wharton School Working Paper.Google Scholar
  24. 24.
    Lee, Y., Park, I., Cho, S., & Choi, J. (2018). Smartphone user segmentation based on app usage sequence with neural networks. Telematics and Informatics, 35(2), 329–339.CrossRefGoogle Scholar
  25. 25.
    Lin, J., Sugiyama, K., Kan, M. Y., & Chua, T. S. (2014). New and improved: Modeling versions to improve app recommendation. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval, pp. 647–656. ACM.Google Scholar
  26. 26.
    Liu, B., Kong, D., Cen, L., Gong, N.Z., Jin, H., & Xiong, H. (2015). Personalized mobile app recommendation: Reconciling app functionality and user privacy preference. In Proceedings of the eighth ACM international conference on web search and data mining (pp. 315–324). ACM.Google Scholar
  27. 27.
    Liu, D., Xiang, C., Li, S., Ren, J., Liu, R., Liang, L., et al. (2018). Hinextapp: A context-aware and adaptive framework for app prediction in mobile systems. In Sustainable computing: Informatics and systems.Google Scholar
  28. 28.
    Liu, Z., & Park, S. (2015). What makes a useful online review? Implication for travel product websites. Tourism Management, 47, 140–151.CrossRefGoogle Scholar
  29. 29.
    Ly, D. K., Sugiyama, K., Lin, Z., & Kan, M. Y. (2011). Product review summarization from a deeper perspective. In Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries (pp. 311–314). ACM.Google Scholar
  30. 30.
    Ma, Z., Sun, A., Yuan, Q., & Cong, G.(2012). Topic-driven reader comments summarization. In Proceedings of the 21st ACM international conference on Information and knowledge management (pp. 265–274). ACM.Google Scholar
  31. 31.
    Malhotra, P., Ramakrishnan, A., Anand, G., Vig, L., Agarwal, P., & Shroff, G. (2016). Lstm-based encoder-decoder for multi-sensor anomaly detection. arXiv preprint arXiv:1607.00148.
  32. 32.
    Meire, M., Ballings, M., & Van den Poel, D. (2016). The added value of auxiliary data in sentiment analysis of facebook posts. Decision Support Systems, 89, 98–112.CrossRefGoogle Scholar
  33. 33.
    Mukherjee, A., & Liu, B. (2012). Mining contentions from discussions and debates. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 841–849). ACM.Google Scholar
  34. 34.
    Mukherji, A., Srinivasan, V., & Welbourne, E. (2014). Adding intelligence to your mobile device via on-device sequential pattern mining. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing (pp. 1005–1014). ACM.Google Scholar
  35. 35.
    Netzer, O., Feldman, R., Goldenberg, J., & Fresko, M. (2012). Mine your own business: Market-structure surveillance through text mining. Marketing Science, 31(3), 521–543.CrossRefGoogle Scholar
  36. 36.
    Pimentel, M. A., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014). A review of novelty detection. Signal Processing, 99, 215–249.CrossRefGoogle Scholar
  37. 37.
    Qi, J., Zhang, Z., Jeon, S., & Zhou, Y. (2016). Mining customer requirements from online reviews: A product improvement perspective. Information & Management, 53(8), 951–963.CrossRefGoogle Scholar
  38. 38.
    Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30–40.CrossRefGoogle Scholar
  39. 39.
    Satoh, Y., Nagata, H., Kytömäki, P., & Gerrard, S. (2005). Evaluation of the university library service quality: Analysis through focus group interviews. Performance Measurement and Metrics, 6(3), 183–193.CrossRefGoogle Scholar
  40. 40.
    Schumaker, R. P., Jarmoszko, A. T., & Labedz, C. S, Jr. (2016). Predicting wins and spread in the premier league using a sentiment analysis of twitter. Decision Support Systems, 88, 76–84.CrossRefGoogle Scholar
  41. 41.
    Shi, Y., Wei, F., Yu, K., & Wu, X. (2018). App usage prediction based on an extended feature selection method rspec mic. In 2018 IEEE 3rd international conference on big data analysis (ICBDA) (pp. 324–328). IEEE.Google Scholar
  42. 42.
    Srinivasan, V., Moghaddam, S., Mukherji, A., Rachuri, K. K., Xu, C., & Tapia, E. M. (2014). Mobileminer: Mining your frequent patterns on your phone. In Proceedings of the 2014 ACM international joint conference on pervasive and ubiquitous computing (pp. 389–400). ACM.Google Scholar
  43. 43.
    Sun, M. (2012). How does the variance of product ratings matter? Management Science, 58(4), 696–707.CrossRefGoogle Scholar
  44. 44.
    Tsaparas, P., Ntoulas, A., & Terzi, E.(2011). Selecting a comprehensive set of reviews. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 168–176). ACM.Google Scholar
  45. 45.
    Ulwick, A. W. (2003). The strategic role of customer requirements in innovation. Strategyn inc 13, 12.Google Scholar
  46. 46.
    Verhaegen, P. A., Vandevenne, D., Peeters, J., & Duflou, J. R. (2013). Refinements to the variety metric for idea evaluation. Design Studies, 34(2), 243–263.CrossRefGoogle Scholar
  47. 47.
    Verma, P., Singh, P., & Yadava, R. (2017). Fuzzy c-means clustering based outlier detection for saw electronic nose. In 2017 2nd international conference for convergence in technology (I2CT) (pp. 513–519). IEEE.Google Scholar
  48. 48.
    Xiang, Z., Schwartz, Z., Gerdes, J. H, Jr., & Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction? International Journal of Hospitality Management, 44, 120–130.CrossRefGoogle Scholar
  49. 49.
    Xu, X., Lei, Y., & Zhou, X. (2018). A lof-based method for abnormal segment detection in machinery condition monitoring. In 2018 Prognostics and system health management conference (PHM-Chongqing) (pp. 125–128). IEEE.Google Scholar
  50. 50.
    Xu, X., Wang, X., Li, Y., & Haghighi, M. (2017). Business intelligence in online customer textual reviews: Understanding consumer perceptions and influential factors. International Journal of Information Management, 37(6), 673–683.CrossRefGoogle Scholar
  51. 51.
    Yu, I. H., Song, J. J., Ko, J. M., Kim, Y. I. (2010). A survey of customer responses for developing value-added services. In 2010 international conference on control automation and systems (ICCAS) (pp. 815–818). IEEE.Google Scholar
  52. 52.
    Zhang, J., Yin, Z., & Wang, R. (2017). Pattern classification of instantaneous cognitive task-load through gmm clustering, laplacian eigenmap, and ensemble svms. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 14(4), 947–965.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Industrial EngineeringSeoul National UniversitySeoulSouth Korea
  2. 2.Data Driven User Experience team, Mobile Communication LabLG ElectronicsSeoulSouth Korea

Personalised recommendations