Use of radio frequency identification for targeted advertising: A collaborative filtering approach using Bayesian networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4724 LNAI, Issue , 2007, Pages 889-900

  Cinicioglu, Esma Nur ^a   Shenoy, Prakash P ^a   Kocabasoglu, Canan ^a  

^a UNIVERSITY OF KANSAS   (United States)

Author keywords

Bayesian networks; Collaborative filtering; Learning Bayesian networks; RFID; Targeted advertising

Indexed keywords

COMPUTER SUPPORTED COOPERATIVE WORK; DECISION MAKING; ELECTRONIC DOCUMENT EXCHANGE;

LEARNING BAYESIAN NETWORKS; RADIO FREQUENCY IDENTIFICATION (RFID); TARGETED ADVERTISING;

BAYESIAN NETWORKS;

EID: 38149000924     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-75256-1_77     Document Type: Conference Paper

References (29)

1. Adomavicius, G., Tuzhilin, A.: Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans, on Knowledge and Data Engineering 17(6), 734-749 (2005)
2. Breeese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. In: Proceedings of Fourteenth conference on Uncertainty in Artificial Intelligence (1998)
3. Bruno, D., Paché, G.: From Traditional Retailing to E-tailing: The death and Rebirth of the Hypermarket Format? Frontiers of E-Business Research (2005)
4. Cavoukian, A.: Tag, You're It: Privacy Implications of Radio Frequency Identification (RFID) Technology. Information and Privacy Commissioner (2004)
5. Curtin, J., Kaufmann, R.J., Riggins, F.J.: Making the Most Out of RFID Technology: A Research Agenda for the Study of Adaptation, Usage and Impact of RFID. Information Technology and Management (2007)
6. th Int. Conf. on Information Processing and Management of Uncertainty in Knowledge-based Systems (IPMU-06), pp. 2158-2165 (2006)
7. Decker, C., Kubach, U., Beigl, M.: Revealing the Retail Black Box by Interaction Sensing. In: Proceedings of ICDCS 2003, Providence, Rhode Island, pp. 328-333 (2003)
8. Dipert, B.: Reading between the Lines. Business & Company Resource Center (2005)
9. Finkenzeller, K.: RFID Handbook Radio-Frequency Identification and Applications. John Wiley & Son, New York (1999)
10. Heckerman, D., Chickering, D.M., Meek, C., Rounthwaite, R., Kadie, C.: Dependency Networks for Inference, Collaborative Filtering, and Data Visualization. Journal of Machine Learning Research 1, 49-75 (2000)
11. Herlocker, J.L., Konstan, J.A., Riedl, J.: Explaining Collaborative Filtering Recommendations. In: Proceedings of the ACM 2000 Conference on Computer Supported Cooperative Work, pp. 241-250 (2000)
12. Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans.on Information Systems 22(1), 5-53 (2004)
13. Hopwood, J.: What is RFID? Intellident, http://www.intellident.co.uk
14. Inman, J., Winer, R.: Where the Rubber Meets the Road: A Model for In-store Consumer Decision Making. Marketing Science Institute Report, 98-122 (1998)
15. Lee, Y.M., Cheng, F., Leung, Y.T.: Exploring the Impact of RFID on Supply Chain Dynamics. In: Proceedings of the 2004 Winter Simulation Conference (2004)
16. Linden, G., Smith, B., York, J.: Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, 76-80 (2003)
17. Mild, A.: An Improved Collaborative Filtering Approach for Predicting Cross-category Purchases based on Binary Market Basket Data. Adaptive Information Systems and Modelling in Economics and Management Science (2002)
18. Mitai, T.: The Emergence of RFID Technology. ISCR Future Technology Topic Brief (2003)
19. Mittal, B.: An Integrated Framework for Relating Diverse Consumer Characteristics to Supermarket Coupon Redemption. Journal of Marketing Research 31(11), 533-544 (1994)
20. Netflix (2007), http://www.netflixprize.com/
21. Nogués, M.T.M.: Combining Machine Learning and Rule-Based Approaches in Spanish Syntatic Generation. PhD Dissertation, Institut Universitari de Lingüística Aplicada Universitat Pompeu Fabra (2006)
22. Pazzani, M.J.: A Framework for Collaborative, Content-Based and Demographic Filtering. Artificial Intelligence Review (1999)
23. th Conf. on Uncertainty in AI (UAI-01) (2001)
24. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An Open Architecture for Collaborative Filtering of Netnews. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work, pp. 175-186 (1994)
25. Rutner, S., Waller, M.A., Mentzer, J.T.: A Practical Look at RFID. Supply Chain Management Review (2004)
26. Schneider, M.: Towards a Transparent Proactive User Interface for a Shopping Assistant. In: Workshop on Multi-user and Ubiquitous User Interfaces, pp. 31-35 (2004)
27. Sweeney II, P.J.: RFID for Dummies. Wiley, Chichester (2005)
28. Yu, K., Schwaighofer, A., Tresp, V., Xiaowei, X., Kriegel, H.P.: Probabilistic Memorybased Collaborative Filtering. IEEE Transaction on Knowledge and Data Engineering 15(1), 56-69 (2004)
29. th Ann. Int'l ACM SIGIR Conf., pp. 294-302. ACM Press, New York (2001)