English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

User portrait analysis is one of the key points in human behavior analysis. It is important to describe or guess user's characteristics through rational methods in business analysis. In this paper, we use the user details records data set from a mobile operator to analyze the preference of users with different brand phones for different APPs and propose the concept of mobile Internet life personas (MILP) and the latent MILP indexing (LMILPI) model for the analysis of users' MILP. At the same time, we build user portrait analysis framework based on the latent semantic indexing (LSI) theme model, LMILPI model, and association rule mining. On the one hand, we analyze users' preference for APP content when using different mobile brands. On the other hand, we analyze the relationship among mobile brands, user access time, and MILPs to describe users' Internet behavior. Our research shows that there is a difference between users who use different brands of mobile phones: 1) users who use different brand phones have different preferences for different APPs. However, if mobile brand marketing methods or target users are same or similar, the APP preference of these brands will be similar; 2) MILPs are different between users who use different brands of mobile phones, but the MILPs displayed on Android platform are similar even though brands are not same, while the MILP displayed on iPhone is quite different from MILPs on Android; and 3) MILPs' importance will be changed by mobile phone brands and time periods. The analytical framework which we propose can provide commercial solutions such as application recommendations, market strategy formulation, Internet access, and other fields.

Exploring the Users’ Preference Pattern of Application Services Between Different Mobile Phone Brands