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Online Product Recommendations based on Diversity and Latent Association Analysis on News and Products
Hsing Yu Chen, Yu Chun Lin,
Duen Ren Liu
*
, Tzeng Feng Liu
*
此作品的通信作者
資訊管理研究所
研究成果
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同行評審
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引文 斯高帕斯（Scopus）
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Keyphrases
Online Ratings
100%
Association Analysis
100%
Browsing
60%
User Preference
40%
Click-through
40%
News Websites
40%
Recommendation Diversity
40%
Interest Analysis
40%
Hybrid Method
20%
Content-based
20%
Recommendation Approach
20%
Recommendation Method
20%
Potential Users
20%
Product Recommendation
20%
Collaborative Filtering
20%
Loyalty
20%
Online Products
20%
Data Sparsity
20%
Latent Topic Modeling
20%
Collaborative Content
20%
Online Assessment
20%
Online World
20%
Matrix Factorization
20%
Click-through Rate
20%
Product Interest
20%
Interest Recommendation
20%
Engineering
Experimental Result
100%
Filtration
100%
Sparsity
100%
Matrix Factorization
100%
Hybrid Method
100%
Cold Start
100%
Computer Science
User Preference
100%
Association Analysis
100%
Experimental Result
50%
Sparsity
50%
Collaborative Filtering
50%
Topic Modeling
50%
Matrix Factorization
50%
Online Evaluation
50%