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Recommendation for custom product via probabilistic relevance model
Yue WANG
, M. M. TSENG
Department of Mathematics and Information Technology (MIT)
Research output
:
Chapter in Book/Report/Conference proceeding
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Chapters
1
Citation (Scopus)
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Dive into the research topics of 'Recommendation for custom product via probabilistic relevance model'. Together they form a unique fingerprint.
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Keyphrases
Information Overload
100%
Product Design
100%
Relevance Model
100%
Probabilistic Relevance
100%
Copyright
50%
Adaptation
50%
Recommendation Approach
50%
Product Specification
50%
Recommendation Efficiency
50%
Recommendation Method
50%
New Recommendations
50%
Product Recommendation
50%
Product Variety
50%
Active Consumer
50%
E-commerce Companies
50%
Product Recommendation System
50%
Off-the-shelf Product
50%
Partial Product
50%
Specification-based
50%
Customer Specification
50%
Product Development Practices
50%
Computer Science
Relevance Model
100%
Product Design
100%
Presented Approach
50%
Partial Product
50%
Random Recommendation
50%
Engineering
Product Design
100%
Product Specification
50%