Context-based Knowledge Recommendation: A 3-D Collaborative Filtering Approach
- Resource Type
- Conference
- Authors
- Liang, Kaichun; Cai, Shuqin; Zhao, Qiankun
- Source
- 2007 5th IEEE International Conference on Industrial Informatics Industrial Informatics, 2007 5th IEEE International Conference on. 2:627-632 Jul, 2007
- Subject
- Power, Energy and Industry Applications
Computing and Processing
Collaboration
Context modeling
Information filtering
Information filters
Robustness
Testing
Collaborative work
Technology management
Filtering algorithms
Problem-solving
- Language
- ISSN
- 1935-4576
2378-363X
We propose a novel and enhanced knowledge recommendation approach using 3-D collaborative filtering. Our approach has the following advantages: (1) rather than only use the user-item matrix, the context of filtering is modeled in the third dimension, which makes the recommendation more accurate; (2) the sparseness of the user-item matrix can be partially solved by propagating ratings among the rating matrix with respect to users' backgrounds; (3) the relations between different contexts are embedded in the recommendation as well. Experiments have been done with real data collected in an enterprise knowledge-base for ill-structured problem solving. The results show that our 3D collaborative filtering approach can improve the existing approaches in terms of both the quality of knowledge recommendation and robustness.