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Improving the quality of recommendations for users and items in the tail of distribution
Liang HU
, Longbing CAO
, Jian CAO
, Zhiping GU
,
Guandong XU
, Jie WANG
Offices of the President (P)
Research output
:
Contribution to journal
›
Articles
›
peer-review
23
Citations (Scopus)
Overview
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Dive into the research topics of 'Improving the quality of recommendations for users and items in the tail of distribution'. Together they form a unique fingerprint.
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Keyphrases
Tail Distribution
100%
Recommendation Quality
100%
Long Tail Items
100%
User Preference
66%
Popular Item
66%
Recommender Systems
66%
Short Head
66%
Long Tail
66%
Electronic Commerce
33%
Selling
33%
User Choice
33%
Choice Data
33%
Low Quality Data
33%
Regularization Method
33%
Feedback Data
33%
System Method
33%
Qualitative Prediction
33%
Latent Factor Model
33%
Empirical Priors
33%
Head-tail
33%
Shilling Attack
33%
E-marketing
33%
Comprehensive Effect
33%
Computer Science
User Preference
100%
Recommender Systems
100%
Regularization
100%
Final Estimate
50%
Future Business
50%