9,291 research outputs found
Effective Mechanism for Social Recommendation of News
Recommendation systems represent an important tool for news distribution on
the Internet. In this work we modify a recently proposed social recommendation
model in order to deal with no explicit ratings of users on news. The model
consists of a network of users which continually adapts in order to achieve an
efficient news traffic. To optimize network's topology we propose different
stochastic algorithms that are scalable with respect to the network's size.
Agent-based simulations reveal the features and the performance of these
algorithms. To overcome the resultant drawbacks of each method we introduce two
improved algorithms and show that they can optimize network's topology almost
as fast and effectively as other not-scalable methods that make use of much
more information
Discrete Factorization Machines for Fast Feature-based Recommendation
User and item features of side information are crucial for accurate
recommendation. However, the large number of feature dimensions, e.g., usually
larger than 10^7, results in expensive storage and computational cost. This
prohibits fast recommendation especially on mobile applications where the
computational resource is very limited. In this paper, we develop a generic
feature-based recommendation model, called Discrete Factorization Machine
(DFM), for fast and accurate recommendation. DFM binarizes the real-valued
model parameters (e.g., float32) of every feature embedding into binary codes
(e.g., boolean), and thus supports efficient storage and fast user-item score
computation. To avoid the severe quantization loss of the binarization, we
propose a convergent updating rule that resolves the challenging discrete
optimization of DFM. Through extensive experiments on two real-world datasets,
we show that 1) DFM consistently outperforms state-of-the-art binarized
recommendation models, and 2) DFM shows very competitive performance compared
to its real-valued version (FM), demonstrating the minimized quantization loss.
This work is accepted by IJCAI 2018.Comment: Appeared in IJCAI 201
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