Location Based Recommendation for Mobile Users Using Language Model and Skyline Query

Full Text (PDF, 309KB), PP.19-28

Views: 0 Downloads: 0

Author(s)

Qiang Pu 1,2,3,* Ahmed Lbath 3 Daqing He 4

1. School of Information Science and Technology, Chengdu University, Chengdu, China

2. Key Lab of Pattern Recognition & Intelligent Information Processing, Chengdu University, Chengdu, China

3. Joseph Fourier University of Grenoble, LIG-MRIM, Grenoble, France

4. School of Information Sciences of University of Pittsburgh, Pittsburgh, USA

* Corresponding author.

DOI: https://doi.org/10.5815/ijitcs.2012.10.02

Received: 27 Nov. 2011 / Revised: 14 Mar. 2012 / Accepted: 16 May 2012 / Published: 8 Sep. 2012

Index Terms

Location-Based Service, Mobile Information Recommendation, Language Model, Skyline Query, Implicit Preference

Abstract

Location based personalized recommendation has been introduced for the purpose of providing a mobile user with interesting information by distinguishing his preference and location. In most cases, mobile user usually does not provide all attributes of his preference or query. In extreme case, especially when mobile user is moving, he even does not provide any preference or query. Meanwhile, the recommendation system database also does not contain all attributes that can express what the user needs. In this paper, we design an effective location based recommendation system to provide the most possible interesting places to a user when he is moving, according to his implicit preference and physical moving location without the user’s providing his preference or query explicitly. We proposed two circle concepts, physical position circle that represents spatial area around the user and virtual preference circle that is a non-spatial area related to user’s interests. Those skyline query places in physical position circle which also match mobile user’s implicit preference in virtual preference circle will be recommended. User’s implicit preference will be estimated under language modeling framework according to user’s historical visiting behaviors. Experiments show that our method is effective in recommending interesting places to mobile users. The main contribution of the paper comes from the combination of using skyline query and information retrieval to do an implicit location-based personalized recommendation without user’s providing explicit preference or query.

Cite This Paper

Qiang Pu, Ahmed Lbath, Daqing He, "Location Based Recommendation for Mobile Users Using Language Model and Skyline Query", International Journal of Information Technology and Computer Science(IJITCS), vol.4, no.10, pp.19-28, 2012. DOI: DOI:10.5815/ijitcs.2012.10.02

Reference

[1]Lbath, A. Method and Device for Automatic Production of Context Aware Mobile Services. Pat. N° WO 006721, 2005. http://patentscope.wipo.int/search/en/WO2007006721.

[2]Börzsönyi, S., Kossmann, D., and Stocker, K. The skyline operator. in Proceedings of the 17th International Conference on Data Engineering. 2001. Heidelberg, Germany.

[3]Kodama, K., et al. Skyline Queries Based on User Locations and Preferences for Making Location-Based Recommendations. in ACM LBSN'09. 2009. Seattle, WA, USA.

[4]Boulmakoul, A., L. Karim, and A. Lbath, Moving Object Trajectories Meta-Model And Spatio-Temporal Queries. International Journal of Database Management Systems (IJDMS). 2012. 4(2) 

[5]Huang, Z., et al. Skyline queries against mobile lightweight devices in MANETs. in ICDE'06. 2006.

[6]Huang, X. and C.S. Jensen. In-Route Skyline Querying for Location-Based Services. in LNCS 3428. 2005.

[7]Raper, J., Geographic relevance. Journal of Documentation, 2007. 63(6): p. 836-852.

[8]Kuo, M.-H., Chen, L.-C. , and Liang, C.-W. Building and Evaluating a Location-Based Service Recommendation System with a Preference Adjustment Mechanism. Expert Systems with Applications, 2009. 36: p. 3543–3554.

[9]Pu, Q. and He, D. Semantic clustering based relevance language model. Information Technology Journal, 2009. 9(2): p. 236-246.

[10]Lavrenko, V. and Allan, J. Realtime query expansion in relevance models. in IR 473. 2006. University of Massachusetts.

[11]Kurland, O., Lee, L., and Domshlak, C. Better than the real thing? Iterative pseudo-query processing using cluster-based language models, in Proc. 28th ACM SIGIR conference on Research and Development in Information Retrieval. 2005: August 15-19. p. 19-26.

[12]Kossmann, D., Ramsak, F., and Rost, S. Shooting Stars in the Sky: An Online Algorithm for Skyline Queries. in Proceedings of the 28th VLDB Conference. 2002. HongKong, China.

[13]Hyvärinen, A., Survey on independent component analysis, in Neural Computing Surveys 2. 1999. p. 94-128.

[14]Kolenda, T., Clustering text using independent component analysis. 2002, Inst. of Informatics and Mathematical Modeling, Tech. University of Denmark.

[15]Zhukov, L. and Gleich, D. Topic indentification in soft clustering using PCA and ICA, in Technical report. 2004, Yahoo Research Labs.

[16]Pu, Q., et al., Information recommendation using improved feature selection and independent component analysis. Journal of Computational Information Systems, 2007. Vol. 3(4): p. 1731-1738.

[17]Salton, G., and Buckley, B. Term-weighting approaches in automatic text retrieval. Information Processing and Management, 1988. 24: p. 513-523.

[18]Lavrenko, V., and Croft, W.B. Relevance-based language models, in Proc. 24th ACM SIGIR conference on Research and Development in Information Retrieval. 2001: New Orleans, LA, USA. p. 120-127.

[19]Ponte, J., and Croft, W.B. A language modeling approach to information retrieval. in Proc. 21st ACM SIGIR conference on Research and Development in Information Retrieval. 1998. Melbourne, Australia.

[20]Kolenda, T., et al., Dtu:toolbox, in http://isp.imm.dtu.dk /toolbox/. 2002: Internet site, Informatics and Mathematical Modeling, Technical University of Denmark.