339-353 Mining usage traces of mobile apps for dynamic preference prediction

Zhung Xun Liao, Wen-Chih Peng, Philip S. Yu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Due to a huge amount of mobile applications (abbreviated as Apps), for Apps providers, the usage preferences of Apps are important in recommending Apps, downloading Apps and promoting Apps.We predict and quantize users' dynamic preferences by exploring their usage traces of Apps. To address the dynamic preference prediction problem, we propose Mode-based Prediction (abbreviated as MBP) and Reference-based Prediction (abbreviated as RBP) algorithms. Both MBP and RBP consist of two phases: the trend detection phase and the change estimation phase. In the trend detection phase, both algorithms determine whether the preference of an App is increasing or decreasing. Then, in the change estimation phase, the amount of preference change is calculated. In particular, MBP adopts users' current usage mode (active or inactive), and then estimates the amount of change via our proposed utility model. On the other hand, RBP calculates an expected number of usage as a reference, and then builds a probabilistic model to estimate the change of preference by comparing the real usage and the reference. We conduct comprehensive experiments using two App usage traces and one music listening log, the Last.fm dataset, to validate our proposed algorithms. The experimental results show that both MBP and RBP outperform the usage-based method that is based solely on the number of usages.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 17th Pacific-Asia Conference, PAKDD 2013, Proceedings
Pages339-353
Number of pages15
EditionPART 1
DOIs
StatePublished - 1 Dec 2013
Event17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013 - Gold Coast, QLD, Australia
Duration: 14 Apr 201317 Apr 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 1
Volume7818 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2013
CountryAustralia
CityGold Coast, QLD
Period14/04/1317/04/13

Keywords

  • Apps
  • Dynamic preference prediction
  • Mobile applications

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