TY - JOUR
AU - Tian, Jilei
AB -  Mining Mobile User Preferences for Personalized Context-Aware Recommendation HENGSHU ZHU and ENHONG CHEN, University of Science and Technology of China HUI XIONG, Rutgers University KUIFEI YU and HUANHUAN CAO, Nokia Research Center JILEI TIAN, Nokia Recent advances in mobile devices and their sensing capabilities have enabled the collection of rich contextual information and mobile device usage records through the device logs. These context-rich logs open a venue for mining the personal preferences of mobile users under varying contexts and thus enabling the development of personalized context-aware recommendation and other related services, such as mobile online advertising. In this article, we illustrate how to extract personal context-aware preferences from the context-rich device logs, or context logs for short, and exploit these identified preferences for building personalized contextaware recommender systems. A critical challenge along this line is that the context log of each individual user may not contain sufficient data for mining his or her context-aware preferences. Therefore, we propose to first learn common context-aware preferences from the context logs of many users. Then, the preference of each user can be represented as a distribution of these common context-aware preferences. Specifically, we develop two approaches for mining common context-aware preferences 
TI - Mining Mobile User Preferences for Personalized Context-Aware Recommendation
JF - ACM Transactions on Intelligent Systems and Technology (TIST)
DO - 10.1145/2532515
DA - 2014-12-18
UR - https://www.deepdyve.com/lp/association-for-computing-machinery/mining-mobile-user-preferences-for-personalized-context-aware-IuSylzLOFk
SP - 1
VL - 5
IS - 4
DP - DeepDyve
ER -