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Item-based collaborative filtering recommendation algorithms
Badrul Sarwar
,
George Karypis
,
Joseph A Konstan
, John Riedl
Computer Science and Engineering
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
8278
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Scopus citations
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Dive into the research topics of 'Item-based collaborative filtering recommendation algorithms'. Together they form a unique fingerprint.
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Keyphrases
Item-based
100%
Item-based Collaborative Filtering
100%
Collaborative Filtering Recommendation Algorithm
100%
User-centric
66%
Recommendation System
66%
K-nearest
66%
Avail
33%
System Technology
33%
Regression Model
33%
Information Services
33%
High Coverage
33%
Item Correlation
33%
Collaborative Filtering Systems
33%
Personalized Recommendation
33%
Data Sparsity
33%
Large-scale Problems
33%
Cosine Similarity
33%
Item Similarity
33%
Generation Algorithm
33%
Information Product
33%
Applied Knowledge
33%
Recommender
33%
Item-based Algorithm
33%
Visitor numbers
33%
Item Vectors
33%
Weighted Sums
33%
Products or Services
33%
Very Large Scale
33%
Better Performance
33%
User-Item Matrix
33%
Knowledge Discovery Techniques
33%
Live Interaction
33%
Computer Science
Collaborative Filtering
100%
Recommender Systems
100%
Recommendation Algorithm
100%
Knowledge Discovery
50%
Sparsity
50%
Large-Scale Problem
50%
Cosine Similarity
50%
Information Product
50%
collaborative filtering algorithm
50%
Ble
50%
Collaboration
50%