Model updating is important for mobile data mining tasks. When more labeled data are available for a given target user, the mobile data mining algorithm should incorporate such data in order to provide more accurate services for the user. However, directly re-training the model with both the old and the new data would be resource consuming especially for mobile applications. A more desired way is to incrementally update the model in an online fashion. In this chapter, we introduce an online model for the mobile data mining tasks. The online model is orthogonal with the hierarchical model and personalized model. The basic idea is to adopt the stochastic sub-gradient descent method and updates the learning models with a small portion of new data.

Original languageEnglish (US)
Title of host publicationSpringerBriefs in Computer Science
Number of pages8
Publication statusPublished - Jan 1 2018

Publication series

NameSpringerBriefs in Computer Science
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776


ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Yao, Y., Su, X., & Tong, H. (2018). Online Model. In SpringerBriefs in Computer Science (pp. 43-50). (SpringerBriefs in Computer Science). Springer. https://doi.org/10.1007/978-3-030-02101-6_6