Abstract

Another challenge for mobile data mining tasks is personalization. On one hand, we usually do not have plenty labels for a certain user, and thus we need to borrow the labeled data from other users. One the other hand, different users tend to behavior differently and thus have different patterns of sensor readings, making data borrowing non-trivial. In this chapter, we introduce a personalized treatment for mobile data mining tasks. The basic idea can be divided into two stages. In the first stage, for a given target user, we select and borrow some data from the users whose labeled data are already collected in the database. In the second stage, we reweight the borrowed data and use them as the training data for the target user. The proposed method is able to estimate the sample distributions, and then reweight the samples based on the estimated distributions so as to minimize the model loss with respect to the target user’s data.

Original languageEnglish (US)
Title of host publicationSpringerBriefs in Computer Science
PublisherSpringer
Pages31-41
Number of pages11
DOIs
StatePublished - Jan 1 2018

Publication series

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

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ASJC Scopus subject areas

  • Computer Science(all)

Cite this

Yao, Y., Su, X., & Tong, H. (2018). Personalized Model. In SpringerBriefs in Computer Science (pp. 31-41). (SpringerBriefs in Computer Science). Springer. https://doi.org/10.1007/978-3-030-02101-6_5