303
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Content Prioritization Based on Usage Pattern Analysis

& ORCID Icon

References

  • Alberts, W. A., & Van der Geest, T. M. (2011). Color matters: Color as trustworthiness cue in web sites. Technical Communication, 58(2), 149–160. http://www.ingentaconnect.com/content/stc/tc/2011/00000058/00000002/art00004
  • Amiriparian, S., Freitag, M., Cummins, N., & Schuller, B. (2017). Sequence to sequence autoencoders for unsupervised representation learning from audio. Universität Augsburg.
  • Andrade, O. D., & Novick, D. G. (2008). Expressing help at appropriate levels. In Proceedings of the 26th annual acm international conference on design of communication (pp. 125–130). Association for Computing Machinery.
  • Barker, T. T. (1998). Writing software documentation. A Task-oriented Approach.
  • Burns, W., Chen, L., Nugent, C., Donnelly, M., Skillen, K. L., & Solheim, I. (2013). Mining usage data for adaptive personalisation of smartphone based help-on-demand services. In Proceedings of the 6th international conference on pervasive technologies related to assistive environments (p. 39). Association for Computing Machinery.
  • Chang, S., Han, W., Tang, J., Qi, G.-J., Aggarwal, C. C., & Huang, T. S. (2015). Heterogeneous network embedding via deep architectures. In Proceedings of the 21th acm sigkdd international conference on knowledge discovery and data mining (pp. 119–128). Association for Computing Machinery.
  • Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., & Abbeel, P. (2016). Infogan: Interpretable representation learning by information maximizing generative adversarial nets. arXiv preprint arXiv:1606.03657. https://arxiv.org/abs/1606.03657
  • Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv Preprint arXiv:1406.1078. https://arxiv.org/abs/1406.1078
  • Crane, D. (2005). A dozen techniques to improve your software online help. https://www.drexplain.com/press/articles/a_dozen_techniques_to_improve_your_software_online_help/
  • d’Alessandro, C., & Trucco, P. C. (2011). Business potential and market opportunities of intelligent lbss for personal mobility–a european case study. Procedia Computer Science, 5, 906–911. https://doi.org/10.1016/j.procs.2011.07.126
  • Dai, H., Umarov, R., Kuwahara, H., Li, Y., Song, L., Gao, X., & Stegle, O. (2017). Sequence2vec: A novel embedding approach for modeling transcription factor binding affinity landscape. Bioinformatics, 33(22), 3575–3583. https://doi.org/10.1093/bioinformatics/btx480
  • 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). Association for Computing Machinery
  • Ellison, M. (2014). Seven golden rules of online help design. Aquent.
  • Gan, M., & Xiao, K. (2019). R-rnn: Extracting user recent behavior sequence for click-through rate prediction. IEEE Access, 7, 111767–111777. https://doi.org/10.1109/ACCESS.2019.2927717
  • Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., … Bengio, Y. (2014). Generative adversarial nets. In Advances in neural information processing systems (pp. 2672–2680). Montreal, Canada.
  • Graves, A., Liwicki, M., Fernández, S., Bertolami, R., Bunke, H., & Schmidhuber, J. (2009). A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(5), 855–868. https://doi.org/10.1109/TPAMI.2008.137
  • Halbach, T., & Schulz, T. (n.d.). Mobilesage–a prototype based case study for delivering context-aware, accessible, and personalized on-demand help content. International Journal of Advances in Intelligent Systems, 7(2), 267–278. http://www.iariajournals.org/intelligent_systems/tocv7n12.html
  • Halsted, K. L., & Roberts, J. H. J. (2002). Eclipse help system: An open source user as- sistance offering. In Proceedings of the 20th annual international conference on computer documentation (pp. 49–59). Association for Computing Machinery.
  • Insights, B., & Insights, C. (2017). Customer segmentation. Bain & Company.
  • Jang, M., Seo, S., & Kang, P. (2019). Recurrent neural network-based semantic variational autoencoder for sequence-to-sequence learning. Information Sciences, 490, 59–73. https://doi.org/10.1016/j.ins.2019.03.066
  • Kohlhase, A. E., & Kohlhase, M. (2009). Modeling task experience in user assistance systems. In Proceedings of the 27th acm international conference on design of communication (pp. 135–142). Association for Computing Machinery.
  • Lee, Y., Cho, S., & Choi, J. (2019a). Determining user needs through abnormality detection and heterogeneous embedding of usage sequence. Electronic Commerce Research, 1–17. https://doi.org/10.1007/s10660-019-09347-6
  • Lee, Y., Cho, S., & Choi, J. (2019b). Smartphone help contents re-organization considering user specification via conditional gan. International Journal of Human-Computer Studies, 129, 108–115. https://doi.org/10.1016/j.ijhcs.2019.04.002
  • 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. https://doi.org/10.1016/j.tele.2017.12.007
  • Linder, J. (2015). How to develop a help system for a communication app. Linkoping University.
  • Liu, Z., Zheng, V. W., Zhao, Z., Li, Z., Yang, H., Wu, M., & Ying, J. (2018). Interactive paths embedding for semantic proximity search on heterogeneous graphs. In Proceedings of the 24th acm sigkdd international conference on knowledge discovery & data mining (pp. 1860–1869). Association for Computing Machinery.
  • 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. https://arxiv.org/abs/1607.00148
  • Oppermann, R. (2017). Adaptive user support: Ergonomic design of manually and automati- cally adaptable software. Routledge.
  • Quadrana, M., Cremonesi, P., & Jannach, D. (2018). Sequence-aware recommender systems. ACM Computing Surveys (CSUR), 51(4), 1–36. https://doi.org/10.1145/3190616
  • Roy, M. C., Rannou, Y., & Rivard, L. (2007). The design of effective online help in web applications. Journal of Knowledge Management Practice, 8, 2. http://www.tlainc.com/articl136.htm
  • Rumelhart, D. E., Smolensky, P., McClelland, J. L., & Hinton, G. (1986). Sequential thought processes in pdp models. Parallel Distributed Processing: Explorations in the Microstructures of Cognition, 2, 3–57. https://ieeexplore.ieee.org/book/6276825
  • Sato, D., Morimura, T., Katsuki, T., Toyota, Y., Kato, T., & Takagi, H. (2016). Automated help system for novice older users from touchscreen gestures. In Pattern recognition (icpr), 2016 23rd international conference on (pp. 3073–3078). Institute of Electrical and Electronics Engineers
  • Shi, C., Hu, B., Zhao, W. X., & Philip, S. Y. (2019). Heterogeneous information network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering, 31(2), 357–370. https://doi.org/10.1109/TKDE.2018.2833443
  • Stamper, J., Barnes, T., Lehmann, L., & Croy, M. (2008). The hint factory: Automatic generation of contextualized help for existing computer aided instruction. In Proceedings of the 9th international conference on intelligent tutoring systems young researchers track (pp. 71–78). Springer.
  • Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. arXiv preprint arXiv:1409.3215. https://arxiv.org/abs/1409.3215
  • Tarus, J. K., Niu, Z., & Yousif, A. (2017). A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Generation Computer Systems, 72, 37–48. https://doi.org/10.1016/j.future.2017.02.049
  • Zhou, Y., Huang, C., Hu, Q., Zhu, J., & Tang, Y. (2018). Personalized learning full-path recommendation model based on lstm neural networks. Information Sciences, 444, 135–152. https://doi.org/10.1016/j.ins.2018.02.053
  • Zhuang, Z., Kong, X., Rundensteiner, E., Zouaoui, J., & Arora, A. (2019). Attributed sequence embedding. arXiv Preprint arXiv:1911.00949. https://arxiv.org/abs/1911.00949

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.