665 research outputs found
MLI: An API for Distributed Machine Learning
MLI is an Application Programming Interface designed to address the
challenges of building Machine Learn- ing algorithms in a distributed setting
based on data-centric computing. Its primary goal is to simplify the
development of high-performance, scalable, distributed algorithms. Our initial
results show that, relative to existing systems, this interface can be used to
build distributed implementations of a wide variety of common Machine Learning
algorithms with minimal complexity and highly competitive performance and
scalability
Scalable and interpretable product recommendations via overlapping co-clustering
We consider the problem of generating interpretable recommendations by
identifying overlapping co-clusters of clients and products, based only on
positive or implicit feedback. Our approach is applicable on very large
datasets because it exhibits almost linear complexity in the input examples and
the number of co-clusters. We show, both on real industrial data and on
publicly available datasets, that the recommendation accuracy of our algorithm
is competitive to that of state-of-art matrix factorization techniques. In
addition, our technique has the advantage of offering recommendations that are
textually and visually interpretable. Finally, we examine how to implement our
technique efficiently on Graphical Processing Units (GPUs).Comment: In IEEE International Conference on Data Engineering (ICDE) 201
Fast Conical Hull Algorithms for Near-separable Non-negative Matrix Factorization
The separability assumption (Donoho & Stodden, 2003; Arora et al., 2012)
turns non-negative matrix factorization (NMF) into a tractable problem.
Recently, a new class of provably-correct NMF algorithms have emerged under
this assumption. In this paper, we reformulate the separable NMF problem as
that of finding the extreme rays of the conical hull of a finite set of
vectors. From this geometric perspective, we derive new separable NMF
algorithms that are highly scalable and empirically noise robust, and have
several other favorable properties in relation to existing methods. A parallel
implementation of our algorithm demonstrates high scalability on shared- and
distributed-memory machines.Comment: 15 pages, 6 figure
Fast Matrix Factorization for Online Recommendation with Implicit Feedback
This paper contributes improvements on both the effectiveness and efficiency
of Matrix Factorization (MF) methods for implicit feedback. We highlight two
critical issues of existing works. First, due to the large space of unobserved
feedback, most existing works resort to assign a uniform weight to the missing
data to reduce computational complexity. However, such a uniform assumption is
invalid in real-world settings. Second, most methods are also designed in an
offline setting and fail to keep up with the dynamic nature of online data. We
address the above two issues in learning MF models from implicit feedback. We
first propose to weight the missing data based on item popularity, which is
more effective and flexible than the uniform-weight assumption. However, such a
non-uniform weighting poses efficiency challenge in learning the model. To
address this, we specifically design a new learning algorithm based on the
element-wise Alternating Least Squares (eALS) technique, for efficiently
optimizing a MF model with variably-weighted missing data. We exploit this
efficiency to then seamlessly devise an incremental update strategy that
instantly refreshes a MF model given new feedback. Through comprehensive
experiments on two public datasets in both offline and online protocols, we
show that our eALS method consistently outperforms state-of-the-art implicit MF
methods. Our implementation is available at
https://github.com/hexiangnan/sigir16-eals.Comment: 10 pages, 8 figure
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