Personalized local search using ontology based user profile category model

Document Type : Research Paper


1 Computer Applications, PSG College of Technology, Coimbatore, India

2 Applied Mathematics and Computational Sciences, PSG College of Technology, Coimbatore, India


The personalized local mobile search aims at finding the right on the spot information that is most relevant to the user's requests. It is implemented as a mobile application where the user can access nearby places based on his/ her current location. In Today's technology driven world user profiles are the virtual representation of each user and they include a variety of user information such as personal, interest and preference data. These profiles are the outcome of the user profiling process and they are essential to service personalization. The user profile based personalization approach can be applied to enhance the power of mobile local search for local spots and contributes to a significant convenience in location-based mobile searching. The system takes the user information such as personal, health, entertainment and choice of preference and these parameters are passed to Google Maps API key for personalized query processing. As a result, the user will get prominent services rather than closing one.


[1] J. Allan, J. Aslam, N. Belkin, C. Buckley, J. Callan, B. Croft and C. Zhai, Challenges in information retrieval
and language modeling, Report of a Workshop Held at the Center for Intelligent Information Retrieval, University
of Massachusetts Amherst, ACM SIGIR Forum, 37(1) (2003) 31–47.
[2] J. Bao, Y. Zheng and M.F. Mokbel, Location-based and preference-aware recommendation using sparse geo-social
networking data, Proc. 20th Int. Conf. Adv. Geog. Inf. Syst. (2012) 199–208.
[3] O. Bouidghaghen, L. Tamine and M. Boughanem, Context-aware user’s interests for personalizing mobile search,
Mobile Data Manag. (MDM), 12th IEEE Int. Conf. MDM 1 (2011) 129–134.
[4] S. Ilarri, E. Mena and A. Illarramendi, Location-dependent query processing: Where we are and where we are
heading, ACM Computing Surv. 42(3) (2010) 1–73.
[5] N. Ilayaraja and M. Jane, Answering closest-pair nearest neighbor using voronoi diagram for location dependent
information system in mobile environment, Int. J. Appl. Eng. Res. 10(3) (2015) 7133–7145.
[6] N. Ilayaraja, F.M.M. Jane, M. Safar and R. Nadarajan, WARM based data pre-fetching and cache replacement
strategies for location dependent information system in wireless environment, Springer: Wireless Personal Commun. (2016) 1–32.
[7] N. Ilayaraja, F.M.M. Jane, I. Thomson, C.V. Narayan, R. Nadarajan and M. Safar, Semantic data caching
strategies for location dependent data in mobile environments, Int. Conf. Digit. Info. Commun. Tech. Appl. (2011)
[8] N. Ilayaraja, N. Mary, F. Ashwin, R. Karthikeyani and P. Karthikeyan, Service type based cache replacement
policy for location dependent data in mobile environments, ICMCM (2009).[9] M. Kamvar and S. Baluja, A large scale study of wireless search behavior: google mobile search, Proc. Int. Conf.
for Human Computer Interaction, Canada, (2006) 22–27.
[10] M.F. Mokbel and J.J. Levandoski, Toward context and preference-aware location-based services, Proc. Eighth
ACM Int. Workshop on Data Engin. Wireless and Mobile Access, ACM (2009) 25–32.
[11] V. Roto, Search on mobile phones, Comput. J. American Soc. Info. Sci. Tech. 57(6) (2006) 834–837.
[12] X. Shen, B. Tan and C. Zhai, Ucair: Capturing and exploiting context for personalized search, Proc. ACM SIGIR
2005 Workshop on Information Retrieval in Context (IRiX), (2005).
[13] K.L. Skillen, L. Chen, C.D. Nugent, M.P. Donnelly and I. Solheim, A user profile ontology based approach for
assisting people with dementia in mobile environments, Conf. Proc. IEEE Eng. Med. Biol. Soc. (2012) 6390–6393.
[14] A. Soffer, Y. Maarek and B.-W. Chang, WWW2002 workshop on mobile search, ACM SIGMOD Record 31 (2002)
Volume 12, Special Issue
December 2021
Pages 1693-1702
  • Receive Date: 02 October 2021
  • Accept Date: 21 November 2021