Skip to search form
Skip to main content
Skip to account menu
Semantic Scholar
Semantic Scholar's Logo
Search 229,043,043 papers from all fields of science
Search
Sign In
Create Free Account
Collaborative filtering
Known as:
Cf
, Collaborative Filter
, Shilling attacks
Collaborative filtering (CF) is a technique used by recommender systems. Collaborative filtering has two senses, a narrow one and a more general one…
Expand
Wikipedia
(opens in a new tab)
Create Alert
Alert
Related topics
Related topics
49 relations
Apache Mahout
Apache Spark
Business logic
California Report Card
Expand
Broader (2)
Collaborative software
Collective intelligence
Papers overview
Semantic Scholar uses AI to extract papers important to this topic.
2015
2015
A Multi-dimensional Comparison of Toolkits for Machine Learning with Big Data
Aaron N. Richter
,
T. Khoshgoftaar
,
Sara Landset
,
Tawfiq Hasanin
IEEE International Conference on Information…
2015
Corpus ID: 16123465
Big data is a big business, and effective modeling of this data is key. This paper provides a comprehensive multidimensional…
Expand
Highly Cited
2011
Highly Cited
2011
An item-oriented recommendation algorithm on cold-start problem
T. Qiu
,
Guang Chen
,
Zi-Ke Zhang
,
Tao Zhou
2011
Corpus ID: 55845675
Based on a hybrid algorithm incorporating the heat conduction and probability spreading processes (Proc. Natl. Acad. Sci. U.S.A…
Expand
Highly Cited
2011
Highly Cited
2011
Text Text Revolution: A Game That Improves Text Entry on Mobile Touchscreen Keyboards
Dmitry Rudchenko
,
Tim Paek
,
Eric Badger
International Conference on Pervasive Computing
2011
Corpus ID: 2176475
Mobile devices often utilize touchscreen keyboards for text input. However, due to the lack of tactile feedback and generally…
Expand
Highly Cited
2010
Highly Cited
2010
Collaborative Filtering for People to People Recommendation in Social Networks
Xiongcai Cai
,
Michael Bain
,
+4 authors
Ashesh Mahidadia
Australasian Conference on Artificial…
2010
Corpus ID: 17571119
Predicting people other people may like has recently become an important task in many online social networks. Traditional…
Expand
2010
2010
Improving Prediction Accuracy in Trust-Aware Recommender Systems
Sanjog Ray
,
A. Mahanti
Hawaii International Conference on System…
2010
Corpus ID: 28540195
Trust-aware recommender systems are intelligent technology applications that make use of trust information and user personal data…
Expand
Highly Cited
2009
Highly Cited
2009
An unbiased p-step predictive FIR filter for a class of noise-free discrete-time models with independently observed states
Y. Shmaliy
Signal, Image and Video Processing
2009
Corpus ID: 15426777
The paper addresses a new unbiased p-step toward predictive finite impulse response (FIR) filter for a class of discrete-time…
Expand
2006
2006
Leveraging Active Learning for Relevance Feedback Using an Information Theoretic Diversity Measure
Charlie K. Dagli
,
S. Rajaram
,
Thomas S. Huang
ACM International Conference on Image and Video…
2006
Corpus ID: 11044386
Interactively learning from a small sample of unlabeled examples is an enormously challenging task. Relevance feedback and more…
Expand
Highly Cited
2005
Highly Cited
2005
Mining changes in customer buying behavior for collaborative recommendations
Y. Cho
,
Y. Cho
,
S. Kim
Expert systems with applications
2005
Corpus ID: 14347919
2005
2005
A Fuzzy Relational Approach to Event Recommendation
C. Cornelis
,
Xuetao Guo
,
Jie Lu
,
Guanquang Zhang
Indian International Conference on Artificial…
2005
Corpus ID: 11972923
Most existing recommender systems employ collaborative fil- tering (CF) techniques in making projections about which items an e…
Expand
2003
2003
Convergent algorithms for collaborative filtering
J. Kleinberg
,
M. Sandler
ACM Conference on Economics and Computation
2003
Corpus ID: 496575
A collaborative filtering system analyzes data on the past behavior of its users so as to make recommendations --- a canonical…
Expand
By clicking accept or continuing to use the site, you agree to the terms outlined in our
Privacy Policy
(opens in a new tab)
,
Terms of Service
(opens in a new tab)
, and
Dataset License
(opens in a new tab)
ACCEPT & CONTINUE