Context-aware Recommender Systems using Data Mining Techniques
This study proposes a novel recommender system to
provide the advertisements of context-aware services. Our proposed
model is designed to apply a modified collaborative filtering (CF)
algorithm with regard to the several dimensions for the personalization
of mobile devices – location, time and the user-s needs type. In
particular, we employ a classification rule to understand user-s needs
type using a decision tree algorithm. In addition, we collect primary
data from the mobile phone users and apply them to the proposed
model to validate its effectiveness. Experimental results show that the
proposed system makes more accurate and satisfactory advertisements
than comparative systems.
[1] Schilke, SW, Bleimann, U, Furnell, SM, Phippen, AD (2004)
Multi-dimensional personalisation for location and interest-based
recommendation. Internet Research 14(5):379-385
[2] Balabanovic, M, Shoham, Y (1997) Fab: Content-based, collaborative
recommendation. Communications of ACM 40(3):66-72
[3] Billsus, D, Pazzani, MJ (1998) Learning collaborative information filters.
In: Proceedings of the 15th ICML, Madison, WI, pp 46-54
[4] Lawrence, RD, Almasi, GS, Kotlyar, V, Viveros, MS, Duri, SS (2001)
Personalization of supermarket product recommendations. Data Mining
and Knowledge Discovery 5(1-2):11-32
[5] Schafer, JB, Konstan, J, Riedl, J (2001) Electronic commerce
recommender applications. Data Mining and Knowledge Discovery
5(1-2):115-152
[6] Maclnnis, DJ, Jaworski, BJ (1989) Information processing from
advertisements: Toward an integrative framework. Journal of Marketing
53(4):1-23
[7] Roh, TH, Oh, KJ, Han, I (2003) The collaborative filtering
recommendation based on SOM cluster-indexing CBR. Expert Systems
with Applications 25(3):413-423
[8] Breese, JS, Heckerman, D, Kadie, C (1998) Empirical analysis of
predictive algorithms for collaborative filtering. In: Proceedings of the
14th Annual Conference on Uncertainty in Artificial Intelligence, San
Francisco, CA, pp. 43-52
[9] Sarwar, BM, Konstan, JA, Borchers, A, Herlocker, J, Miller, B, Riedl, J
(1998) Using filtering agents to improve prediction quality in the
GroupLens research collaborative filtering system. In: Proceedings of
ACM Conference on Computer Supported Cooperative Work (CSCW),
Seattle, WA, pp. 345-354
[10] Goldberg, K, Roeder, T, Gupta, D, Perkins, C (2001) Eigentaste: a
constant time collaborative filtering algorithm. Information Retrieval
4(2):133-151
[11] Green, SB, Salkind, NJ, Akey, TM (2000) Using SPSS for Windows. 2
Ed. Prentice Hall, NJ
[1] Schilke, SW, Bleimann, U, Furnell, SM, Phippen, AD (2004)
Multi-dimensional personalisation for location and interest-based
recommendation. Internet Research 14(5):379-385
[2] Balabanovic, M, Shoham, Y (1997) Fab: Content-based, collaborative
recommendation. Communications of ACM 40(3):66-72
[3] Billsus, D, Pazzani, MJ (1998) Learning collaborative information filters.
In: Proceedings of the 15th ICML, Madison, WI, pp 46-54
[4] Lawrence, RD, Almasi, GS, Kotlyar, V, Viveros, MS, Duri, SS (2001)
Personalization of supermarket product recommendations. Data Mining
and Knowledge Discovery 5(1-2):11-32
[5] Schafer, JB, Konstan, J, Riedl, J (2001) Electronic commerce
recommender applications. Data Mining and Knowledge Discovery
5(1-2):115-152
[6] Maclnnis, DJ, Jaworski, BJ (1989) Information processing from
advertisements: Toward an integrative framework. Journal of Marketing
53(4):1-23
[7] Roh, TH, Oh, KJ, Han, I (2003) The collaborative filtering
recommendation based on SOM cluster-indexing CBR. Expert Systems
with Applications 25(3):413-423
[8] Breese, JS, Heckerman, D, Kadie, C (1998) Empirical analysis of
predictive algorithms for collaborative filtering. In: Proceedings of the
14th Annual Conference on Uncertainty in Artificial Intelligence, San
Francisco, CA, pp. 43-52
[9] Sarwar, BM, Konstan, JA, Borchers, A, Herlocker, J, Miller, B, Riedl, J
(1998) Using filtering agents to improve prediction quality in the
GroupLens research collaborative filtering system. In: Proceedings of
ACM Conference on Computer Supported Cooperative Work (CSCW),
Seattle, WA, pp. 345-354
[10] Goldberg, K, Roeder, T, Gupta, D, Perkins, C (2001) Eigentaste: a
constant time collaborative filtering algorithm. Information Retrieval
4(2):133-151
[11] Green, SB, Salkind, NJ, Akey, TM (2000) Using SPSS for Windows. 2
Ed. Prentice Hall, NJ
@article{"International Journal of Business, Human and Social Sciences:57434", author = "Kyoung-jae Kim and Hyunchul Ahn and Sangwon Jeong", title = "Context-aware Recommender Systems using Data Mining Techniques", abstract = "This study proposes a novel recommender system to
provide the advertisements of context-aware services. Our proposed
model is designed to apply a modified collaborative filtering (CF)
algorithm with regard to the several dimensions for the personalization
of mobile devices – location, time and the user-s needs type. In
particular, we employ a classification rule to understand user-s needs
type using a decision tree algorithm. In addition, we collect primary
data from the mobile phone users and apply them to the proposed
model to validate its effectiveness. Experimental results show that the
proposed system makes more accurate and satisfactory advertisements
than comparative systems.", keywords = "Location-based advertisement, Recommender system,Collaborative filtering, User needs type, Mobile user.", volume = "4", number = "4", pages = "398-6", }