3,141 research outputs found
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
Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks
Factorization Machines (FMs) are a supervised learning approach that enhances
the linear regression model by incorporating the second-order feature
interactions. Despite effectiveness, FM can be hindered by its modelling of all
feature interactions with the same weight, as not all feature interactions are
equally useful and predictive. For example, the interactions with useless
features may even introduce noises and adversely degrade the performance. In
this work, we improve FM by discriminating the importance of different feature
interactions. We propose a novel model named Attentional Factorization Machine
(AFM), which learns the importance of each feature interaction from data via a
neural attention network. Extensive experiments on two real-world datasets
demonstrate the effectiveness of AFM. Empirically, it is shown on regression
task AFM betters FM with a relative improvement, and consistently
outperforms the state-of-the-art deep learning methods Wide&Deep and DeepCross
with a much simpler structure and fewer model parameters. Our implementation of
AFM is publicly available at:
https://github.com/hexiangnan/attentional_factorization_machineComment: 7 pages, 5 figure
Neural Collaborative Filtering
In recent years, deep neural networks have yielded immense success on speech
recognition, computer vision and natural language processing. However, the
exploration of deep neural networks on recommender systems has received
relatively less scrutiny. In this work, we strive to develop techniques based
on neural networks to tackle the key problem in recommendation -- collaborative
filtering -- on the basis of implicit feedback. Although some recent work has
employed deep learning for recommendation, they primarily used it to model
auxiliary information, such as textual descriptions of items and acoustic
features of musics. When it comes to model the key factor in collaborative
filtering -- the interaction between user and item features, they still
resorted to matrix factorization and applied an inner product on the latent
features of users and items. By replacing the inner product with a neural
architecture that can learn an arbitrary function from data, we present a
general framework named NCF, short for Neural network-based Collaborative
Filtering. NCF is generic and can express and generalize matrix factorization
under its framework. To supercharge NCF modelling with non-linearities, we
propose to leverage a multi-layer perceptron to learn the user-item interaction
function. Extensive experiments on two real-world datasets show significant
improvements of our proposed NCF framework over the state-of-the-art methods.
Empirical evidence shows that using deeper layers of neural networks offers
better recommendation performance.Comment: 10 pages, 7 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
A Graphical Model Formulation of Collaborative Filtering Neighbourhood Methods with Fast Maximum Entropy Training
Item neighbourhood methods for collaborative filtering learn a weighted graph
over the set of items, where each item is connected to those it is most similar
to. The prediction of a user's rating on an item is then given by that rating
of neighbouring items, weighted by their similarity. This paper presents a new
neighbourhood approach which we call item fields, whereby an undirected
graphical model is formed over the item graph. The resulting prediction rule is
a simple generalization of the classical approaches, which takes into account
non-local information in the graph, allowing its best results to be obtained
when using drastically fewer edges than other neighbourhood approaches. A fast
approximate maximum entropy training method based on the Bethe approximation is
presented, which uses a simple gradient ascent procedure. When using
precomputed sufficient statistics on the Movielens datasets, our method is
faster than maximum likelihood approaches by two orders of magnitude.Comment: ICML201
Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation
Hashing is an effective technique to address the large-scale recommendation
problem, due to its high computation and storage efficiency on calculating the
user preferences on items. However, existing hashing-based recommendation
methods still suffer from two important problems: 1) Their recommendation
process mainly relies on the user-item interactions and single specific content
feature. When the interaction history or the content feature is unavailable
(the cold-start problem), their performance will be seriously deteriorated. 2)
Existing methods learn the hash codes with relaxed optimization or adopt
discrete coordinate descent to directly solve binary hash codes, which results
in significant quantization loss or consumes considerable computation time. In
this paper, we propose a fast cold-start recommendation method, called
Multi-Feature Discrete Collaborative Filtering (MFDCF), to solve these
problems. Specifically, a low-rank self-weighted multi-feature fusion module is
designed to adaptively project the multiple content features into binary yet
informative hash codes by fully exploiting their complementarity. Additionally,
we develop a fast discrete optimization algorithm to directly compute the
binary hash codes with simple operations. Experiments on two public
recommendation datasets demonstrate that MFDCF outperforms the
state-of-the-arts on various aspects
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