A Hybrid Knowledge Discovery System Based on Items and Tags

Winyu Niranatlamphong; Worasit Choochaiwattana

doi:10.6000/1929-7092.2017.06.32

Authors

Winyu Niranatlamphong College of Creative Design and Entertainment Technology, Dhurakij Pundit University
Worasit Choochaiwattana College of Creative Design and Entertainment Technology, Dhurakij Pundit University

DOI:

https://doi.org/10.6000/1929-7092.2017.06.32

Keywords:

Collaborative Filtering, Content-based Filtering, Item-Based Recommendation, Tag-Based Recommendation, Knowledge Recommender Service

Abstract

Exponentially increasing knowledge in a management system is the main cause of the overload problem. Development of a recommender service embedded in the management system is challenging. This paper proposes a hybrid approach by combining an item-based recommendation technique (collaborative filtering technique) with a tag-based recommendation technique (content based filtering technique). In order to evaluate the performance of the proposed hybrid approach, a group of knowledge management system users are invited as participants in the research. Participants are asked to use the prototype of a management system embedded within the knowledge recommender service for four months, which guarantees that each interaction by participants with knowledge items are recorded. A confusion matrix is used to compute accuracy of the proposed hybrid approach. The results of the experiments reveal that the hybrid approach outperforms both item-based and tag-based approaches. The hybrid approach seems to be a promising technique for a recommender service in the knowledge management system.

References

Aryal, Jagannath, Ritaban Dutta, and Ahsan Morshed. 2013 “Development of an Intelligent Environmental Knowledge Recommendation System for Sustainable Water Resource Management Using Modis Satellite Imagery.” Proceedings of the 2013 IEEE International Geoscience & Remote Sensing Symposium. pp. 2204–2207.
https://doi.org/10.1109/igarss.2013.6723253
Bruke, Robin. 2007 “Hybrid Web Recommender System.” The Adaptive Web. Springer. Pp. 377-408.
Choochaiwattana, Worasit. 2015. “A Comparison Between Item-based and Tag-based Recommendation on a Knowledge Management System: A Preliminary Investigation.” Interna-tional Journal of Information and Education Technology. 5:754-757.
https://doi.org/10.7763/IJIET.2015.V5.605
Davoodi, Fatemeh G. and Omid Fatemi. 2012. “Tag Based Recommender System for Social Bookmarking Sites.” Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. pp. 934-940.
https://doi.org/10.1109/asonam.2012.166
Deshpande, Mukund and George Karypis. 2004. “Item-based Top-N Recommendation Algorithms.” ACM Transaction on Information Systems. 22(1):143–177.
https://doi.org/10.1145/963770.963776
Goldberg, David, David. Nichols, Brian M. Oki, and Douglas Terry. 1992. “Using Collaborative Filtering to Weave an Information Tapestry.” Communications of ACM. 35:61-70.
https://doi.org/10.1145/138859.138867
Goldberg, Ken, Theresa Roeder, Dhruv Gupta, and Chris Perkins. 2001. “Eigentaste: A Constant Time Collaborative Filtering Algorithm.” Information Retrieval. 4(2):133-151.
https://doi.org/10.1023/A:1011419012209
Huang, Zhenxing, Xudong Lu, Huilong Duan, and Chenhui Zhao. 2012. “Collaboration-based Medical Knowledge Recom-mendation.” Artificial Intelligence in Medicine. 55(1):13–24.
https://doi.org/10.1016/j.artmed.2011.10.002
Jain, Sarika, Anjali Grover, Praveen S. Thakur, and Sourabh K. Choudhary. 2015. “Trends, Problems and Solutions of Recommender System.” Proceedings of the International Conference on Computing, Communication and Automation. pp. 955-958.
https://doi.org/10.1109/CCAA.2015.7148534
Li, Hong, Lu Liu, and Chenggong Lv. 2006. “Knowledge Recommendation Services Based on Knowledge Interest Group.” Proceedings of the International Conference on Service Systems and Service Management. pp. 162–166.
https://doi.org/10.1109/icsssm.2006.320606
Liang, Kaichun, Shuqin Cai, and Qiankun Zhao. 2007. “Context-based Knowledge Recommendation: A 3-D Collaborative Filtering Approach.” Proceedings of the 5th IEEE International Conference on Industrial Informatics. pp. 627–632.
https://doi.org/10.1109/indin.2007.4384846
Linden, Greg, Brent Smith, and Jeremy York. 2003. “Amazon.com Recommendations: Item-to-Item Collaborative Filtering.” IEEE Internet Computing. 7:76–80.
https://doi.org/10.1109/MIC.2003.1167344
Prasad, RVVSV and V. Valli Kumari. 2012. “A Categorical Review of Recommender Systems.” International Journal of Distributed and Parallel Systems. 3(5):73-83.
https://doi.org/10.5121/ijdps.2012.3507
Resnick, Paul and Hal R. Varian. 1997. “Recommender Systems,” Communications of the ACM, March, pp. 56-58.
https://doi.org/10.1145/245108.245121
Resnick, Paul, Neophytos Iacovou, Mitesh Suchak, Peter Bergsrom, and John Riedl. 1994. “Grouplens: An Open Architecture for Collaborative Filtering of Netnews.” Proceedings of the ACM Conference on Computer Supported Cooperative Work. pp. 175-186.
https://doi.org/10.1145/192844.192905
Ricci, Francesco, Lior Rokach, and Bracha Shapira. 2011. “Introduction to Recommender System Handbook.” Recommender Systems Handbook. Springer. Pp. 1-35.
Si, Luo and Rong Jin. 2003. “Flexible Mixture Model for Collaborative Filtering.” Proceedings of the 20th International Conference on Machine Learning. pp. 704–711.
Vizcaino, Aurora, Javier Portillo-Rodriguez, Juan P. Soto, Mario Piattini, and Oliver Kusche. 2009. “A Recommendation Algorithm for Knowledge Objects Based on a Trust Model.” Proceedings of the IEEE International Conference on Research Challenges in Information Science. pp. 93–102.
https://doi.org/10.1109/rcis.2009.5089272
Zhao, Wenzhao, Jun Wang, and Guannan Lui. 2009. “A Knowledge Recommendation Algorithm Based on Content Syndication.” Proceedings of the 4th International Conference on Computer Sciences and Convergence Information Technology. pp. 742–745.
https://doi.org/10.1109/iccit.2009.153