A Framework for Personalized Multi-Device Information Communicating System
Due to the mobility of users, many information
systems are now developed with the capability of supporting retrieval
of information from both static and mobile users. Hence, the
amount, content and format of the information retrieved will need to
be tailored according to the device and the user who requested for it.
Thus, this paper presents a framework for the design and
implementation of such a system, which is to be developed for
communicating final examination related information to the
academic community at one university in Malaysia. The concept of
personalization will be implemented in the system so that only highly
relevant information will be delivered to the users. The
personalization concept used will be based on user profiling as well
as context. The system in its final state will be accessible through cell
phones as well as intranet connected personal computers.
[1] Light, M., and Maybury, M.T. 2002. Personalized Multimedia
Information Access, Communications of the ACM. 45, 5, 54-59.
[2] Baraglia, R. & Silvestri, F. 2007. Dynamic Personalization of Web Sites
without User Intervention, Communications of the ACM. 50, 2, 63-67.
[3] Zanker, M. 2008. A collaborative constraint-based meta-level
recommender. In Proceedings of the 2008 ACM Conference on
Recommender Systems (Lausanne, Switzerland, October 23 - 25, 2008).
RecSys 2008. 139-146.
[4] Chung, R., Sundaram, D., Srinivasan, A. 2007. Integrated Personal
Recommender Systems. In Proceedings of the Ninth International
Conference on Electronic Commerce (Minneapolis, USA, August 19-22,
2007) ICEC07. 65-74.
[5] Deshpande, M., and Karypis, G. 2004. Item-based top-N Recommender
Algorithms, ACM Transactions on Information Systems (TOIS). 22,
1(Jan. 2004), 143-177.
[6] Teo, C.B., and Gay, K.L. 2006. A Knowledge-Driven Model to
Personalize E-Learning. Journal of Educational Resources in Computing
(JERIC). 6, 1 (Mar. 2006), Article 3.
[7] Czyzewicz, S. 2005. Personalizing the visitor experience with dynamic
information systems. Journal of Computing Sciences in Colleges. 20, 5
(May 2005), 124 - 125.
[8] Weiβ, D., Scheuerer, J., Wendeler, M., Erk, A., G├╝lbahar, M., and
Linnhoff-Popien, C. 2008. A user profile-based personalization system
for digital multimedia content. In Proceedings of the 3rd International
Conference on Digital Interactive Media in Entertainment and Arts
(Athens, Greece, September 10-12, 2008). DIMEA2008. 281-288.
[9] Ntawanga, F., Calitz, A.P., and Barnard, L. 2007. Maintaining Customer
Profiles in E-Commerce Environment. In Proceedings of the 2008
annual research conference of the South African Institute of Computer
Scientists and Information Technologists on IT research in developing
countries: riding the wave of technology (Wilderness, South Africa,
October 6-8, 2008). SAICSIT2008. 169-179.
[10] Wedig, S., and Maddani, O. 2006. A large-scale analysis of query logs
for accessing personalization opportunities. In Proceedings of the 12th
ACM SIGKDD International Conference on Knowledge Discovery and
Data Mining (Philadelphia, USA, August 20-23, 2006). KDD2006. 742-
747.
[11] Ho, S.Y., Davern, M.J., and Tam, K.Y. 2008. Personalization and choice
behavior: the role of personality traits. ACM SIGMIS Database. 39, 4
(Nov. 2008), 31-47.
[12] Xue, G-R, Han, J., Yu, Y., and Yang, Q. 2009. User language model for
collaborative personalized search. ACM Transactions on Information
Systems (TOIS). 27, 2 (Feb. 2009), Article no. 11.
[13] Bellekens, P., Houben, H-J., Aroyo, L., Schaap, K., and Kaptein, A.
2009. User Model Ellicitation and Enrichment for Context-sensitive
Personalization in a Multiplatform TV Environment. In Proceedings of
the 7th European Conference on European Interactive Television
Conference (Leuven, Belgium, June 3-5, 2009). EuroITV2009. 119-128.
[14] Panayiotou, C., Andreou, M., and Samaras, G. 2006. Using Time and
Activity in Personalization for the Mobile User. In Proceedings of the
5th ACM International Workshop on Data Engineering for Wireless and
Mobile Access (Chicago, Illinois, June 25, 2006). MobiDE-06. 87-90.
[15] Bardram, J.E. 2004. Applications of Context-Aware Computing in
Hospital Work - Examples and Design Principles. In Proceedings of the
19th Annual ACM Symposium on Applied Computing (Nicosia, Cyprus,
March 14 -17, 2004). SAC2004. 1574-1579.
[16] Thawani, A., Gopalan, S. and Sridhar, V. 2004. Web-based Context
Aware Information Retrieval in Contact Centers. In Proceedings of the
IEEE/WIC/ACM International Conference on Web Intelligence (Beijing,
China, September 20-24, 2004). WI-04.
[17] Paton, N.W., Diaz, O. 1999. Active Database Systems. ACM Computing
Surveys (CSUR). 31, 1, 63-103.
[18] Wong, T., Henderson, T., and Katz, R.H. 2002. Tunable reliable
multicast for periodic information dissemination, Mobile Networks and
Applications. 7, 1, 21-36.
[19] Armstrong, T., Trescases, O, Amza, C., and de Lara, E. 2006. Efficient
and transparent dynamic content updates for mobile clients. In
Proceedings of the 4th International Conference on Mobile Systems,
Applications and Services (Uppsala, Sweden, June 19-22, 2006).
MobiSys2006. 56 - 68.
[1] Light, M., and Maybury, M.T. 2002. Personalized Multimedia
Information Access, Communications of the ACM. 45, 5, 54-59.
[2] Baraglia, R. & Silvestri, F. 2007. Dynamic Personalization of Web Sites
without User Intervention, Communications of the ACM. 50, 2, 63-67.
[3] Zanker, M. 2008. A collaborative constraint-based meta-level
recommender. In Proceedings of the 2008 ACM Conference on
Recommender Systems (Lausanne, Switzerland, October 23 - 25, 2008).
RecSys 2008. 139-146.
[4] Chung, R., Sundaram, D., Srinivasan, A. 2007. Integrated Personal
Recommender Systems. In Proceedings of the Ninth International
Conference on Electronic Commerce (Minneapolis, USA, August 19-22,
2007) ICEC07. 65-74.
[5] Deshpande, M., and Karypis, G. 2004. Item-based top-N Recommender
Algorithms, ACM Transactions on Information Systems (TOIS). 22,
1(Jan. 2004), 143-177.
[6] Teo, C.B., and Gay, K.L. 2006. A Knowledge-Driven Model to
Personalize E-Learning. Journal of Educational Resources in Computing
(JERIC). 6, 1 (Mar. 2006), Article 3.
[7] Czyzewicz, S. 2005. Personalizing the visitor experience with dynamic
information systems. Journal of Computing Sciences in Colleges. 20, 5
(May 2005), 124 - 125.
[8] Weiβ, D., Scheuerer, J., Wendeler, M., Erk, A., G├╝lbahar, M., and
Linnhoff-Popien, C. 2008. A user profile-based personalization system
for digital multimedia content. In Proceedings of the 3rd International
Conference on Digital Interactive Media in Entertainment and Arts
(Athens, Greece, September 10-12, 2008). DIMEA2008. 281-288.
[9] Ntawanga, F., Calitz, A.P., and Barnard, L. 2007. Maintaining Customer
Profiles in E-Commerce Environment. In Proceedings of the 2008
annual research conference of the South African Institute of Computer
Scientists and Information Technologists on IT research in developing
countries: riding the wave of technology (Wilderness, South Africa,
October 6-8, 2008). SAICSIT2008. 169-179.
[10] Wedig, S., and Maddani, O. 2006. A large-scale analysis of query logs
for accessing personalization opportunities. In Proceedings of the 12th
ACM SIGKDD International Conference on Knowledge Discovery and
Data Mining (Philadelphia, USA, August 20-23, 2006). KDD2006. 742-
747.
[11] Ho, S.Y., Davern, M.J., and Tam, K.Y. 2008. Personalization and choice
behavior: the role of personality traits. ACM SIGMIS Database. 39, 4
(Nov. 2008), 31-47.
[12] Xue, G-R, Han, J., Yu, Y., and Yang, Q. 2009. User language model for
collaborative personalized search. ACM Transactions on Information
Systems (TOIS). 27, 2 (Feb. 2009), Article no. 11.
[13] Bellekens, P., Houben, H-J., Aroyo, L., Schaap, K., and Kaptein, A.
2009. User Model Ellicitation and Enrichment for Context-sensitive
Personalization in a Multiplatform TV Environment. In Proceedings of
the 7th European Conference on European Interactive Television
Conference (Leuven, Belgium, June 3-5, 2009). EuroITV2009. 119-128.
[14] Panayiotou, C., Andreou, M., and Samaras, G. 2006. Using Time and
Activity in Personalization for the Mobile User. In Proceedings of the
5th ACM International Workshop on Data Engineering for Wireless and
Mobile Access (Chicago, Illinois, June 25, 2006). MobiDE-06. 87-90.
[15] Bardram, J.E. 2004. Applications of Context-Aware Computing in
Hospital Work - Examples and Design Principles. In Proceedings of the
19th Annual ACM Symposium on Applied Computing (Nicosia, Cyprus,
March 14 -17, 2004). SAC2004. 1574-1579.
[16] Thawani, A., Gopalan, S. and Sridhar, V. 2004. Web-based Context
Aware Information Retrieval in Contact Centers. In Proceedings of the
IEEE/WIC/ACM International Conference on Web Intelligence (Beijing,
China, September 20-24, 2004). WI-04.
[17] Paton, N.W., Diaz, O. 1999. Active Database Systems. ACM Computing
Surveys (CSUR). 31, 1, 63-103.
[18] Wong, T., Henderson, T., and Katz, R.H. 2002. Tunable reliable
multicast for periodic information dissemination, Mobile Networks and
Applications. 7, 1, 21-36.
[19] Armstrong, T., Trescases, O, Amza, C., and de Lara, E. 2006. Efficient
and transparent dynamic content updates for mobile clients. In
Proceedings of the 4th International Conference on Mobile Systems,
Applications and Services (Uppsala, Sweden, June 19-22, 2006).
MobiSys2006. 56 - 68.
@article{"International Journal of Electrical, Electronic and Communication Sciences:53066", author = "Rohiza Ahmad and Rozana Kasbon and Eliza Mazmee Mazlan and Aliza Sarlan", title = "A Framework for Personalized Multi-Device Information Communicating System", abstract = "Due to the mobility of users, many information
systems are now developed with the capability of supporting retrieval
of information from both static and mobile users. Hence, the
amount, content and format of the information retrieved will need to
be tailored according to the device and the user who requested for it.
Thus, this paper presents a framework for the design and
implementation of such a system, which is to be developed for
communicating final examination related information to the
academic community at one university in Malaysia. The concept of
personalization will be implemented in the system so that only highly
relevant information will be delivered to the users. The
personalization concept used will be based on user profiling as well
as context. The system in its final state will be accessible through cell
phones as well as intranet connected personal computers.", keywords = "System framework, personalization, informationcommunicating system, multi-device.", volume = "4", number = "2", pages = "262-4", }
