Context-Aware Ranking Method for Information Recommendation
Previously, we proposed two recommendation systems—context-aware information filtering (C-IF) and context-aware collaborative filtering (C-CF)—both of which are context-aware recommendation methods. We have also shown their effectiveness through of experiments using a restaurant recommendation system based on these methods. Furthermore, we need to rank the recommended contents to improve the performance of the C-IF and the C-CF. A ranking method ranks the recommended contents based on content parameters that the user regards as important. However, what parameter is important for the user is according to their contexts. For example, a user likes a reasonable restaurant when he is alone, but on the other hand, he likes an expensive and stylish restaurant when he is with his girlfriend. Therefore, it is important to consider his contexts when ranking recomended contents. In this study, we propose a context-aware ranking method. The system ranks recommended contents within users’ contexts.We also evaluate our proposal method from experimental results.
Unable to display preview. Download preview PDF.
- 1.Chen G and Kotz D (2000) A Survey of Context-Aware Mobile Computing Research, Paper TR2000-381, Department of Computer Science, Dartmouth CollegeGoogle Scholar
- 2.Asthana A, Cravatts M and Krzyzanowski P (1994) An Indoor Wireless System for Personalized Shopping Assistance, Proceedings of IEEE Workshop on Mobile Computing Systems and Applications, Santa Cruz, California, IEEE Computer Society Press, pp. 69–74Google Scholar
- 3.Long S, Kooper R, Abowd GD and Atkeson CG (1996) Rapid Prototyping of Mobile Context-Aware Applications: the Cyberguide Case Study, Proceedings of the Second Annual International Conference on Mobile Computing and Networking, White Plains, NY, ACM Press, pp. 97–107Google Scholar
- 4.Davies N, Cheverst K, Mitchell K and Friday A (1999) Caches in the Air: Disseminating Tourist Information in the GUIDE System, Proceedings of Second IEEE Workshop on Mobile Computing Systems and Applications, New Orleans, Louisiana, IEEE Computer Society PressGoogle Scholar
- 5.Oku K, Nakajima S, Miyazaki J and Uemura S (2006) Context-Aware SVM for Context-Dependent Information Recommendation, Proc. of International Workshop on Future Mobile and Ubiquitous Information Technologies (FMUIT2006), pp. 119–122Google Scholar
- 6.Oku K, Nakajima S, Miyazaki J and Uemura S (2007) Investigation for Designing of Context-Aware Recommendation System Using SVM, Proc. of The International MultiConference of Engineers and Computer Scientists 2007 (IMECS 2007), MarchGoogle Scholar
- 8.WEKA: http://www.cs.waikato.ac.nz/ml/weka/ (2006-12-29 confirmed)
- 9.Yahoo! Gourmet, http://gourmet.yahoo.co.jp/gourmet/ (2006-12-29 confirmed)
- 10.Chang C-C and Lin C-J (2006) LIBSVM: a Library for Support Vector MachinesGoogle Scholar