IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Modern Information Retrieval
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
A Concept-Driven Algorithm for Clustering Search Results
IEEE Intelligent Systems
Genetic-Based EM Algorithm for Learning Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Web searcher interaction with the Dogpile.com metasearch engine
Journal of the American Society for Information Science and Technology
Keyword Generation for Search Engine Advertising
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Graph Visualization Techniques for Web Clustering Engines
IEEE Transactions on Visualization and Computer Graphics
The Google Similarity Distance
IEEE Transactions on Knowledge and Data Engineering
Context recognition using internet as a knowledge base
Journal of Intelligent Information Systems
A personalized search engine based on Web-snippet hierarchical clustering
Software—Practice & Experience
Advertising keyword suggestion based on concept hierarchy
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Decision support for team staffing: An automated relational recommendation approach
Decision Support Systems
Cross-lingual audio-to-text alignment for multimedia content management
Decision Support Systems
Machine learning techniques for business blog search and mining
Expert Systems with Applications: An International Journal
Answering aggregate keyword queries on relational databases using minimal group-bys
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Mining comparative opinions from customer reviews for Competitive Intelligence
Decision Support Systems
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Term suggestion is a kind of information retrieval technique that attempts to suggest relevant terms to help users formulate more effective queries and reduce unnecessary search steps. In this paper, we apply two semantic analysis methods, the probabilistic analysis model and semantic analysis graph, to design a term suggestion system that can effectively deal with the problems of synonymy and polysemy. The main contributions of this paper are the following. First, we apply two semantic analysis methods to design a high-performance term suggestion system. Second, we design an intelligent mechanism that can effectively balance cost and performance to minimize the number of iterations required for our system.