Hybrid Approach for a Knowledge Recommender Service: A Combination of Item-Based and Tag-Based Recommendation



An exponentially increasing of knowledge in a knowledge management system is the main cause of the knowledge overload problem. A development of knowledge recommender service embedded in the knowledge management system becomes a challenging task. This paper proposed a hybrid approach by combining an item-based recommendation technique, also known as a collaborative filtering technique, with a tag-based recommendation technique, also known as a content-based filtering technique. To evaluate the performance of the proposed hybrid approach, a group of knowledge management system users were invited to be participants in this research study. As a criterion, the participants were asked to use the prototype of a knowledge management system embedded with the knowledge recommender service for 6 months. This would guarantee that each participant’s interaction with knowledge items could be recorded. A confusion matrix was then used to compute an accuracy of the proposed hybrid approach. The result of the experiment revealed that the proposed hybrid approach outperformed the item-based approach and the tag-based approach. Hence, the proposed hybrid approach was a promising technique for a knowledge recommender service in the knowledge management system.


Collaborative filtering, content-based filtering, item-based recommendation, tag-based recommendation, knowledge recommender service

Full Text:



F Ricci, L Rokach and B Shapira. Introduction to Recommender System Handbook. In: Recommender Systems Handbook. Springer, 2011, p. 1-35.

R Bruke. Hybrid Web Recommender System. In: The Adaptive Web, Springer, 2007, p. 377-408.

P Resnick and HR Varian. Recommender systems. Commun. ACM 1997; 40, 56-8.

D Goldberg, D Nichols, BM Oki and D Terry. Using collaborative filtering to weave an information tapestry. Commun. ACM 1992; 35, 61-70.

K Goldberg, T Roeder, D Gupta and C Perkins. Eigentaste: A constant time collaborative filtering algorithm. Inform. Retrieval. 2001; 4, 133-51.

P Resnick, N Iacovou, M Suchak, P Bergsrom and J Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In: Proceedings of the ACM Conference on Computer Supported Cooperative Work. North Carolina, USA, 1994, p.175-86.

G Linden, B Smith and J York. Amazon.com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput. 2003; 7, 76-80.

M Deshpande and G Karypis. Item-based top-N recommendation algorithms. J. ACM Trans. Inform. Syst. 2004; 22, 143-77.

FG Davoodi and O Fatemi. Tag based recommender system for social bookmarking sites. In: Proceedings of the ACM International Conference on Advances in Social Networks Analysis and Mining. Istanbul, Turkey, 2012, p. 934-40.

L Si and R Jin. Flexible mixture model for collaborative filtering. In: Proceedings of the 20th International Conference on Machine Learning. Washington DC, USA, 2003, p. 704-11.

S Jain, A Grover, PS Thakur and SK Choudhary. Trends, problems and solutions of recommender system. In: Proceedings of the International Conference on Computing, Communication and Automation. Noida, India, 2015, p. 955-8.

R Prasad and VV Kumari. A categorical review of recommender systems. Int. J. Distrib. Par. Syst. 2012; 3, 73-83.

Z Huang, X Lu, H Duan and C Zhao. Collaboration-based medical knowledge recommendation. Artif. Intell. Med. 2012; 55, 13-24.

H Li, L Liu and C Lv. Knowledge recommendation services based on knowledge interest groups. In: Proceedings of the International Conference on Service Systems and Service Management. Troyes, France, 2006, p. 162-6.

W Zhao, J Wang and G Lui. A knowledge recommendation algorithm based on content syndication. In: Proceedings of the 4th International Conference on Computer Sciences and Convergence Information Technology. Seoul, South Korea, 2009, p. 742-5.

J Aryal, R Dutta and A Morshed. Development of an intelligent environmental knowledge recommendation system for sustainable water resource management using modis satellite imagery. In: Proceedings of the 2013 IEEE International Geoscience & Remote Sensing Symposium. Melbourne, VIC, Australia, 2013, p. 2204-7.

A Vizcaino, J Portillo-Rodriguez, JP Soto, M Piattini and O Kusche. A recommendation algorithm for knowledge objects based on a trust model. In: Proceedings of the IEEE International Conference on Research Challenges in Information Science. Fez, Morocco, 2009, p. 93-102.

K Liang, S Cai and Q Zhao. Context-based knowledge recommendation: A 3-D collaborative filtering approach. In: Proceedings of the 5th IEEE International Conference on Industrial Informatics. Vienna, Austria, 2007, p. 627-32.

W Choochaiwattana. A comparison between item-based and tag-based recommendation on a knowledge management system: A preliminary investigation. Inter. J. Inform. Educ. Tech. 2015; 5, 754-7.


  • There are currently no refbacks.


Online ISSN: 2228-835X


Last updated: 2 August 2017