Context-Aware Recommendation Model based on Mobile Application Analysis Platform

Recently, various types of log data have been collected and used due to the explosive increase of mobile devices. In mobile environment with high portability and mobility, in addition, the user context information is an important factor for recommendation process. This study attempted to analyze usability log data collected from the mobile device through an application analysis platform. We suggested a context-aware recommendation model to recommend mobile applications or contents by recognizing users’ context data. The usability data of applications consist of activities which were active during the use of a mobile device. The features of these activities are related with time, location and device information. A model proposed in this study has a flexible structure which can be selectively used depending on user circumstances and performs a usability patterns of the applications based on the collaborative filtering method.

[1]  George Angelos Papadopoulos,et al.  Please Scroll down for Article Enterprise Information Systems a Survey of Software Adaptation in Mobile and Ubiquitous Computing a Survey of Software Adaptation in Mobile and Ubiquitous Computing , 2022 .

[2]  James Won-Ki Hong,et al.  Usage pattern analysis of smartphones , 2011, 2011 13th Asia-Pacific Network Operations and Management Symposium.

[3]  Rabi Prasad Padhy,et al.  RDBMS to NoSQL: Reviewing Some Next-Generation Non-Relational Database's , 2011 .

[4]  Florian Michahelles,et al.  AppAware: which mobile applications are hot? , 2010, Mobile HCI.

[5]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[6]  Qiang Xu,et al.  Identifying diverse usage behaviors of smartphone apps , 2011, IMC '11.

[7]  Alexandros Karatzoglou,et al.  Climbing the app wall: enabling mobile app discovery through context-aware recommendations , 2012, CIKM '12.

[8]  Kamal Ali,et al.  GetJar mobile application recommendations with very sparse datasets , 2012, KDD.

[9]  Sang-Yong Lee,et al.  A Recommendation System using Context-based Collaborative Filtering , 2011 .

[10]  Divyakant Agrawal,et al.  Big data and cloud computing: current state and future opportunities , 2011, EDBT/ICDT '11.

[11]  Gregor von Laszewski,et al.  Efficient resource management for Cloud computing environments , 2010, International Conference on Green Computing.

[12]  Jae Sik Lee,et al.  Music for My Mood: A Music Recommendation System Based on Context Reasoning , 2006, EuroSSC.

[13]  Guanling Chen,et al.  AppJoy: personalized mobile application discovery , 2011, MobiSys '11.

[14]  Wolfgang Wörndl,et al.  A Hybrid Recommender System for Context-aware Recommendations of Mobile Applications , 2007, 2007 IEEE 23rd International Conference on Data Engineering Workshop.

[15]  Hannu Verkasalo.  Analysis of Smartphone User Behavior , 2010, 2010 Ninth International Conference on Mobile Business and 2010 Ninth Global Mobility Roundtable (ICMB-GMR).

[16]  Samuel Madden,et al.  From Databases to Big Data , 2012, IEEE Internet Comput..

[17]  Yong Zhao,et al.  Cloud Computing and Grid Computing 360-Degree Compared , 2008, GCE 2008.

[18]  Simon Moritz,et al.  Utilizing implicit feedback and context to recommend mobile applications from first use , 2011, CaRR '11.

[19]  Rajkumar Buyya,et al.  Market-Oriented Cloud Computing: Vision, Hype, and Reality for Delivering IT Services as Computing Utilities , 2008, 2008 10th IEEE International Conference on High Performance Computing and Communications.

[20]  Yon Dohn Chung,et al.  Parallel data processing with MapReduce: a survey , 2012, SGMD.

[21]  Antonio Krüger,et al.  Exploring the Design Space of Context-aware Recommender Systems that Suggest Mobile Applications , 2010 .

[22]  Antonio Krüger,et al.  AppFunnel: a framework for usage-centric evaluation of recommender systems that suggest mobile applications , 2013, IUI '13.

[23]  Pabitra Mitra,et al.  Feature weighting in content based recommendation system using social network analysis , 2008, WWW.