2,161 research outputs found
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
On Sampling Strategies for Neural Network-based Collaborative Filtering
Recent advances in neural networks have inspired people to design hybrid
recommendation algorithms that can incorporate both (1) user-item interaction
information and (2) content information including image, audio, and text.
Despite their promising results, neural network-based recommendation algorithms
pose extensive computational costs, making it challenging to scale and improve
upon. In this paper, we propose a general neural network-based recommendation
framework, which subsumes several existing state-of-the-art recommendation
algorithms, and address the efficiency issue by investigating sampling
strategies in the stochastic gradient descent training for the framework. We
tackle this issue by first establishing a connection between the loss functions
and the user-item interaction bipartite graph, where the loss function terms
are defined on links while major computation burdens are located at nodes. We
call this type of loss functions "graph-based" loss functions, for which varied
mini-batch sampling strategies can have different computational costs. Based on
the insight, three novel sampling strategies are proposed, which can
significantly improve the training efficiency of the proposed framework (up to
times speedup in our experiments), as well as improving the
recommendation performance. Theoretical analysis is also provided for both the
computational cost and the convergence. We believe the study of sampling
strategies have further implications on general graph-based loss functions, and
would also enable more research under the neural network-based recommendation
framework.Comment: This is a longer version (with supplementary attached) of the KDD'17
pape
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
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