Reactive Multi-Agent Model for Collaborative Filtering - Based Recommender Systems
Tóm tắt: 16
| 
PDF: 15 
##plugins.themes.academic_pro.article.main##
Author
Từ khóa:
Collaborative filtering
recommender systems
multi - agent systems
reactive multi-agent model
reactive agent
attractive force
repulsive force
Tóm tắt
In recent 20 years, using multi-agent models has been developed in many research fields, especially in social science. These multi-agent models allow simulating and studying a complex part of real world by performing an insilico test, or called real simulation. Recently, some research has also proposed multi-agent model for Information Retrieval problems and has achieved some remarkable results. In this paper, we introduce a reactive multi-agent model as a new approach for recommender systems in order to overcome some common limitations of recommender systems, especially recomputation problems when new data is added to the system. Experimental results also indicate that the proposed model can be applied for recommendation problems and our model performs more stably than collaborative filtering based recommender systems.
Tài liệu tham khảo
-
[1] Badrul Sarwar, George Karypis, Joseph Konstan, John Riedl, Item-Based Collaborative Filtering Recommendation Algorithms, Proceedings of the 10th international conference on World Wide Web, (2001), pp. 285-295.
[2] Bsarry Smyth, Brynjar Gretarsson, Svetlin Bost, Tobias Höllerer, Peerchooser: visual interactive recommendation, Proceedings of the 2008 Conference on Human Factors in Computing Systems, Florence, Italy, (2008), pp. 1085-1088.
[3] Cao Hong Hue, G., A., A multi-agent model for Image browsing and retrieval, Studies in Computational Intelligence Volume 457, (2013), pp. 117-126.
[4] F. Klugl, C.Oechslein, F.Puppe, andA.Dornhaus, Multi-agent modelling in comparison to standard modelling. In F.J. Barros and N. Giambasi, editors, Proceedings of AIS 2002: Artificial Intelligence, Simulation and Planning in High Autonomous Systems, San Diego, CA, (2002), pp. 105–110.
[5] F. Zambonelli and H.V.D. Parunak, From design to intention: signs of a revolution, inProceedings of the first international joint conference on Autonomous agents and multiagent systems, ACM Press, (2002), pp. 455–456.
[6] Fabien Michel, Jacques Ferber, and Alexis Drogoul, Multi-Agent Systems and Simulation: a Survey From the Agents Community’s Perspective, CRC Press, LLC, (2001).
[7] Guerra-Hernández, Alejandro, Amal El Fallah-Seghrouchni and Henry Soldano, Learning in BDI Multi-agent Systems, Computational Logic in Multi-Agent Systems, (2004).
[8] J. O'Donovan, B. Smyth, B. Gretarsson, S. Bostandjiev, and T. Höllerer., PeerChooser: visual interactive recommendation, presented at the CHI '08: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, New York, New York, USA, (2008), pp. 1085–1088.
[9] Laaksonen, J., Koskela, M., Oja, E., PicSOM – Self-organizing image retrieval with MPEG-7 content descriptors, IEEE Transactions on Neural Networks 13(4), (2002), pp. 841-853.
[10] Moghaddam, B., Tian, Q., Lesh, N., Shen C., Huang, T.S., Visualization & User-Modeling for Browsing Personal Photo Libraries, International Journal of Computer Vision 56(1/2), (2004), pp. 109–130.
[11] Nguyen Duy Phuong, Le Quang Thang and Tu Minh Phuong, A Graph-Based Method for Combining Collaborative and Content-Based Filtering, Trends in Artificial Intelligence, Springer, (2008), pp. 859-869.
[12] Renault, V., Organisation de Sociétés d'Agents pour la Visualisation d'Informations Dynamiques, PhD thesis, University Paris 6 (France), (2001).
[13] Ricci F., Rokach L., Shapira B., Kantor P.B, Recommender Systems Handbook, 1st Edition, Springer, 845 p. 20 illus., Hardcover, ISBN: 978-0-387-85819-7, (2011).
[14] Rubner, Y., Guibas, L.J., Tomasi, C., The earth movers distance, multi-dimensional scaling, and color-based image retrieval, In APRA Image Understanding Workshop, (1997), pp. 661-668.
[15] Xiaoyuan Su and Taghi M. Khoshgoftaar, A survey of collaborative filtering techniques, Advances in Artificial Intelligence, (2009), pp. 1–20.
Xem thêm
##plugins.themes.academic_pro.article.sidebar##
Đã Xuất bản
Dec 31, 2015
Download
Cách trích dẫn
Tran Thi Ngoc Trang*, Le Viet Man, Nguyen Minh Duc. “Reactive Multi-Agent Model for Collaborative Filtering - Based Recommender Systems”. Tạp Chí Khoa học Và Công nghệ - Đại học Đà Nẵng, vol 12, số p.h 97.1, Tháng Chạp 2015, tr 80-84, https://jst-ud.vn/jst-ud/article/view/2518.
