Automatically Learning User Preferences for Personalized Service Composition

Business & Economics

• Service Composition 100%
• User Preferences 89%
• Web Services 31%
• Learning to Rank 12%
• Machine Learning 9%
• Reinforcement Learning 8%
• Learning Algorithm 8%
• Shopping 8%
• Functionality 7%
• Prioritization 7%
• Burden 6%
• Leverage 5%
• Trade-offs 5%
• Empirical Study 4%

Engineering & Materials Science

• Web services 63%
• Chemical analysis 45%
• Learning to rank 24%
• Reinforcement learning 17%
• Learning algorithms 14%
• Machine learning 12%
• Specifications 11%