Design and Implementation of User Context aware Recommendation Engine for Mobile using Bayesian Network, Fuzzy Logic and Rule Base

Abstract

Context-aware computing refers to a general class of mobile systems that can sense their physical environment, and adapt their behavior accordingly. Such systems are a component of a ubiquitous computing. Context aware computing makes systems aware of situations of interest, enhances services to users, automates systems and reduces obtrusiveness and customizes and personalizes applications. Mobile phones and PDAs are converging into mobile lifestyle devices that offer a wide range of applications to end users. Many of these applications will have the ability to adapt themselves to the user’s situation, commonly referred to as context awareness. Context-aware services have been introduced into mobile devices, such as cellular phones. Context aware service recommendation engine for mobile is designed to automatically adopt its behavior to changing environment. To achieve this, an important issue to be addressed is how to effectively select services for adaptation according to the user’s current context. In this paper, we propose an intelligent service recommendation model. We formulate the service adaptation process by using artificial intelligence techniques like Bayesian Network, fuzzy logic and rule based reasoning .Bayesian Network to classify the incoming call (high priority call, low priority call and unknown calls), fuzzy linguistic variables and membership degrees to define the context situations, the rules for adopting the policies of implementing a service, fitness degree computation and service recommendation. In addition to this we have proposed maximum to minimum priority based context attributes matching algorithm for rule selection based on fitness degree of rules. The context aware mobile is tested for library and class room scenario to exemplify the proposed service recommendation engine and demonstrate its effectiveness

References

• Capra, L.; Emmerich,W.; Mascolo,C.;"CARISMA: context-aware reflective middleware system for mobile applications"; IEEE Transactions on Software Engineering, Volume:29, Issue:10, pp.929 – 945, Oct.2003
• Baochun Li; Nahrstedt, K.;"A control-based middleware framework for quality-of-service adaptations" IEEE Journal on Selected Areas in Communications, Volume: 17 , Issue: 9 pp. 1632 – 1650 , Sept. 1999
• Jiannong Cao1, Na Xing1,2, Alvin T.S Chan1, Yulin Feng2 , Beihong Jin2 ,” Service Adaptation Using Fuzzy Theory in Context-aware MobileComputing Middleware” , Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA’05)
• Choonsung Shin and Woontack Woo, Member, IEEE “Socially Aware TV Program Recommender for Multiple Viewers” Downloaded on August 9, 2009 at 01:42 from IEEEXplore..
• Doug Williams, Marian F Ursu,, Ian Kegel , “An Emergent Role for TV in Social Communication”.
• Dr Daniel Chandler , Matthew Ruckwood, Mediated Communication “ Will Interactive Television Change the Relationship Between the Viewer and the Television Set? “ , MC10120 10/01/2005.
• Doug Riecken , “Perosonalized views of personalization ,” Comm.ACM ,vol 43,n08,2000.
• Muhammad Ashad Kabir ,Jun Han and Alan Colman , “Modeling and Coordianting Social Interactions in Pervasive Environments “,
• N.Datia ,J Moura-Pires .M.Cardoso ,H.Pita , “Temporal Patterns o fTV Watching For Portugues Viewers “.
• Joe Jeffrey ,”High Fidelity Mathematical Models of Social Systems “, Northen Illinois Unversity.
• Ray van Brandenburg , Master Thesis Faculty of Electrical Engineering, Mathematics and Computer Science, Design and Analysis of Communication Systems (DACS),University of Twente” Towards multi-user personalized TV services,introducing combined RFID Digest authentication” ,Dec2007,Master thesis
• Paul Resnick ,Neophytos Iacovou ,Mitesh Suchak Peter Bergstorm ,and John Riedli ,”Grouplens : An open architecture for collaborative filtering of netnews , “ in Proc.ACM Conf.Computer Supported Cooperative Work ,Chape Hill ,NC,USA ,1994,pp.175 -186.
• Annie Chen ,“Context aware collaborative filtering system : Predicting the user’s preference in the ubiquitous computing environment ,” in Location and Context Awareness,pp.244-253 .Springer ,2005
• Manos Papagelis, Dimitris Plexousakis,” Qualitative analysis of user-based and item-based prediction algorithms for recommendation agent “,Engineering Applications of Artificial Intelligence 18 (2005) 781–789 “www.elsevier.com/locate/engappai
• Manos Papagelis1, 2, Ioannis Rousidis2, Dimitris Plexousakis1, 2, Elias Theoharopoulos3 , “Incremental Collaborative Filtering for Highly- Scalable Recommendation Algorithms”, Work conducted at ICS-FORTH
• SongJie Gong , “A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering “,JOURNAL OF SOFTWARE, VOL. 5, NO. 7, JULY 2010, © 2010 ACADEMY PUBLISHER,doi:10.4304/jsw.5.7.745-752
• Kyusik Park, Jongmoo Choi, and Donghee Lee , “A Single-Scaled Hybrid Filtering Method for IPTV Program Recommendation “ , INTERNATIONAL JOURNAL OF CIRCUITS, SYSTEMS AND SIGNAL PROCESSING , Issue 4, Volume 4, 2010
• J.J. Sandvig and Bamshad Mobasher and Robin Burke , “A Survey of Collaborative Recommendation and the Robustness of Model-Based Algorithms “ , Bulletin of the IEEE Computer Society Technical Committee on Data Engineering , Copyright 2008 IEEE.
• Michael Hahsler , “Developing and Testing Top-N Recommendation Algorithms for 0-1 Data using recommenderlab “ , February 27, 2011 ,
• Amancio Bouza, Gerald Reif, Abraham Bernstein , “Probabilistic Partial User Model Similarity for Collaborative Filtering”.
• SongJie Gong , “ A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering”,Journal Of Software ,Vol .5.No 7,July2010.
• Manos Papgelis ,Dimitris Plexousakis , “Qualitative analysis of user based and item based prediction algorithms for recommendation agents”,Engineering Applications of Artificial Intelligence 18(2005) 781-789 ,www.elsevier.com/locate/engappai
• Daniel Siewiorek, Asim Smailagic, Junichi Furukawa, Neema Moraveji, Kathryn Reiger, and Jeremy Shaffer ,” “SenSay: A Context-Aware Mobile Phone”, Proceedings of the Seventh IEEE International Symposium on Wearable Computers (ISWC’03)
• Miika Valtonen, Antti-Matti Vainio, Jukka Vanhala , “Proactive and Adaptive Fuzzy Profile Controlfor Mobile Phones” , 978-1-4244-3304-9/09/$25.00 ©2009 IEEE
• Lakshmish Ramaswamy1, Deepak P2, Ramana Polavarapu2 Kutila Gunasekera3, Dinesh Garg4, Karthik Visweswariah2, Shivkumar Kalyanaraman , ” CAESAR: A Context-Aware, Social Recommender System for Low-End Mobile Devices”, 2009 Tenth International Conference on Mobile Data Management: Systems, Services and Middleware, 978-0-7695-3650-7/09 $25.00 © 2009 IEEE DOI 10.1109/MDM.2009.66
• Jonna Häkkilä, Jani Mäntyjärvi,” Collaboration in Context-Aware Mobile Phone Applications”, Proceedings of the 38th Hawaii International Conference on System Sciences – 2005, 0-7695-2268-8/05/$20.00 (C) 2005 IEEE
• Seyed Hadi Mirisaee, Abdullah Mohd Zin,” A Framework for Context-Aware University Mobile Organizer”, 2009 International Conference on Future Computer and Communication, 978-0-7695-3591-3/09, DOI 10.1109/ICFCC.2009.84.
• Boris Moltchanov1, Michael Knappmeyer2, Omri Fuchs3, Elio Paschetta1,” Context Management and Reasoning for Adaptive Service Provisioning “,9781-4244-3941-6/09/$25.00 ©2009 IEEE
• Courtney McTavish, Suresh Sankaranarayanan,” Intelligent Agent based Hotel Search &Booking System”, 978-1-4244-6875-1/10/$26.00 ©2010 IEEE
• Ryan Anthony Brown , Dr. Suresh Sankaranarayanan,” Intelligent Agent based Mobile Shopper”, 978-1-4244-3474-9/09/$25.00 ©2009 IEEE
• Ren-Hung Hwang, Shang-Yi Tsai, and Chiung-Ying Wang,” UbiPhone: Human-Centered Ubiquitous Phone System”, Published by the IEEE CS n 1536-1268/09/$25.00 © 2009 IEEE,pervasive computing journal ,, April–June 2009 PERVASIVE computing,pp40-47
• Symbian OS. Available at http://www.symbian.org.
• Paramvir Bahl and Venkata N. Padamanabhan, ”RADAR: An In-Building RF-based User Location and Tracking System”, Microsoft Research, 1999
• F. Barcelo-Arroyo et al, “Indoor Location for Safety Applications using Wireless Networks,” in Proc of the 1st ERCIM Workshop on Mobility, Portugal, 2007
• S. Thongthammachart and H. Olesen, “Bluetooth Enables indoor Mobile Location Services,” in Proc. of the 57th IEEESemiannual Vehicular Technology Conference, 2003.
• Sina Zulkernain, Praveen Madiraju1, Sheikh Iqbal Ahamed, Karl Stamm (Marquette University, Milwaukee, Wisconsin, USA ,” A Mobile Intelligent Interruption Management System “ , Journal of Universal Computer Science, vol. 16, no. 15 (2010), 2060-2080submitted: 31/1/10, accepted: 28/7/10, appeared: 1/8/10 © J.UCS
• Han-Saem Park, Keunhyun Oh, and Sung-Bae Cho , “Bayesian Network-Based High-Level Context Recognition for Mobile Context Sharing in Cyber-Physical System” , Department of Computer Science, Yonsei University, 262 Seongsanno, Sudaemoon-gu, Seoul 120-749, Republic of Korea, Hindawi Publishing Corporation ,International Journal of Distributed Sensor Networks Volume 2011, Article ID 650387, 10 pages doi:10.1155/2011/650387.

Index Terms

Keywords