Improving user recommendation by extracting social topics and interest topics of users in uni-directional social networks

Keyphrases

• Social Networks 100%
• Social Interest 100%
• Social Subject 100%
• User Recommendation 100%
• Followee 100%
• Matrix Factorization 100%
• Social Connection 66%
• Recommendation Framework 66%
• Copyright 33%
• Community-based 33%
• Rapid Growth 33%
• Social Factors 33%
• Population Growth 33%
• Social Community 33%
• User Preference 33%
• Twitter 33%
• New User 33%
• Information Overload 33%
• Probabilistic Topic Model 33%
• Large Margin 33%
• User Adoption 33%
• Factorization Method 33%
• Sina Weibo 33%
• Communities of Interest 33%
• Urn Model 33%
• User Pairing 33%
• Binary Features 33%
• Interest Factor 33%
• LDA Model 33%
• Recommender 33%
• Community-based Methods 33%
• Epinions 33%
• Targeted Users 33%

Computer Science

• Social Network 100%
• Linear Discriminant Analysis 100%
• Topic Interest 100%
• Matrix Factorization 100%
• Social Interest 66%
• Social Connection 66%
• User Preference 33%
• Social Community 33%
• Binary Feature 33%
• Individual User 33%