R
Ramakrishnan Kannan
Researcher at Oak Ridge National Laboratory
Publications - 79
Citations - 1153
Ramakrishnan Kannan is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Non-negative matrix factorization & Matrix decomposition. The author has an hindex of 17, co-authored 73 publications receiving 973 citations. Previous affiliations of Ramakrishnan Kannan include Georgia Institute of Technology & IBM.
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Patent
Collaborative shopping across multiple shopping channels using shared virtual shopping carts
TL;DR: In this article, a system for collaborative shopping comprising shopping devices interconnected by a network and respectively used by shoppers in a collaborative shopping session, and virtual shopping carts respectively associated with the shopping devices for sharing information on items of interest among the shoppers and enabling the shoppers to collaborate on the shopping.
Proceedings ArticleDOI
NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on mapreduce
TL;DR: NIMBLE is presented, a portable infrastructure that has been specifically designed to enable the rapid implementation of parallel ML-DM algorithms and is currently runs on top of Hadoop, which is an open-source MR implementation.
Journal ArticleDOI
Bounded matrix factorization for recommender system
TL;DR: A new improved matrix factorization approach for such a rating matrix, called Bounded Matrix Factorization (BMF), which imposes a lower and an upper bound on every estimated missing element of the rating matrix.
Proceedings ArticleDOI
A high-performance parallel algorithm for nonnegative matrix factorization
TL;DR: A high-performance distributed-memory parallel algorithm that computes the factorization by iteratively solving alternating non-negative least squares (NLS) subproblems for W and H, which maintains the data and factor matrices in memory, uses MPI for interprocessor communication, and provably minimizes communication costs.
Patent
Method, medium, and system for allocating a transaction discount during a collaborative shopping session
TL;DR: In this paper, a collaborative shopping group can be established within a social networking web site, where each individual within the group is able to add additional individuals to the group by adding items from a set of different e-commerce sites.