1,250 research outputs found
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
A Deep Embedding Model for Co-occurrence Learning
Co-occurrence Data is a common and important information source in many
areas, such as the word co-occurrence in the sentences, friends co-occurrence
in social networks and products co-occurrence in commercial transaction data,
etc, which contains rich correlation and clustering information about the
items. In this paper, we study co-occurrence data using a general energy-based
probabilistic model, and we analyze three different categories of energy-based
model, namely, the , and models, which are able to capture
different levels of dependency in the co-occurrence data. We also discuss how
several typical existing models are related to these three types of energy
models, including the Fully Visible Boltzmann Machine (FVBM) (), Matrix
Factorization (), Log-BiLinear (LBL) models (), and the Restricted
Boltzmann Machine (RBM) model (). Then, we propose a Deep Embedding Model
(DEM) (an model) from the energy model in a \emph{principled} manner.
Furthermore, motivated by the observation that the partition function in the
energy model is intractable and the fact that the major objective of modeling
the co-occurrence data is to predict using the conditional probability, we
apply the \emph{maximum pseudo-likelihood} method to learn DEM. In consequence,
the developed model and its learning method naturally avoid the above
difficulties and can be easily used to compute the conditional probability in
prediction. Interestingly, our method is equivalent to learning a special
structured deep neural network using back-propagation and a special sampling
strategy, which makes it scalable on large-scale datasets. Finally, in the
experiments, we show that the DEM can achieve comparable or better results than
state-of-the-art methods on datasets across several application domains
Is Simple Better? Revisiting Non-linear Matrix Factorization for Learning Incomplete Ratings
Matrix factorization techniques have been widely used as a method for
collaborative filtering for recommender systems. In recent times, different
variants of deep learning algorithms have been explored in this setting to
improve the task of making a personalized recommendation with user-item
interaction data. The idea that the mapping between the latent user or item
factors and the original features is highly nonlinear suggest that classical
matrix factorization techniques are no longer sufficient. In this paper, we
propose a multilayer nonlinear semi-nonnegative matrix factorization method,
with the motivation that user-item interactions can be modeled more accurately
using a linear combination of non-linear item features. Firstly, we learn
latent factors for representations of users and items from the designed
multilayer nonlinear Semi-NMF approach using explicit ratings. Secondly, the
architecture built is compared with deep-learning algorithms like Restricted
Boltzmann Machine and state-of-the-art Deep Matrix factorization techniques. By
using both supervised rate prediction task and unsupervised clustering in
latent item space, we demonstrate that our proposed approach achieves better
generalization ability in prediction as well as comparable representation
ability as deep matrix factorization in the clustering task.Comment: version
Collaborative Deep Learning for Recommender Systems
Collaborative filtering (CF) is a successful approach commonly used by many
recommender systems. Conventional CF-based methods use the ratings given to
items by users as the sole source of information for learning to make
recommendation. However, the ratings are often very sparse in many
applications, causing CF-based methods to degrade significantly in their
recommendation performance. To address this sparsity problem, auxiliary
information such as item content information may be utilized. Collaborative
topic regression (CTR) is an appealing recent method taking this approach which
tightly couples the two components that learn from two different sources of
information. Nevertheless, the latent representation learned by CTR may not be
very effective when the auxiliary information is very sparse. To address this
problem, we generalize recent advances in deep learning from i.i.d. input to
non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian
model called collaborative deep learning (CDL), which jointly performs deep
representation learning for the content information and collaborative filtering
for the ratings (feedback) matrix. Extensive experiments on three real-world
datasets from different domains show that CDL can significantly advance the
state of the art
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
Hybrid Collaborative Filtering with Autoencoders
Collaborative Filtering aims at exploiting the feedback of users to provide
personalised recommendations. Such algorithms look for latent variables in a
large sparse matrix of ratings. They can be enhanced by adding side information
to tackle the well-known cold start problem. While Neu-ral Networks have
tremendous success in image and speech recognition, they have received less
attention in Collaborative Filtering. This is all the more surprising that
Neural Networks are able to discover latent variables in large and
heterogeneous datasets. In this paper, we introduce a Collaborative Filtering
Neural network architecture aka CFN which computes a non-linear Matrix
Factorization from sparse rating inputs and side information. We show
experimentally on the MovieLens and Douban dataset that CFN outper-forms the
state of the art and benefits from side information. We provide an
implementation of the algorithm as a reusable plugin for Torch, a popular
Neural Network framework
Analyzing Deep Learning Algorithms for Recommender Systems
As the volume of online information increases, recommender systems have been an effective strategy to overcome information overload by giving selective recommendations based on certain criteria such as user ratings and user interactions. Recommender systems are utilized in a variety of fields, with common examples being music recommendations and product recommendations on E-Commerce websites. These systems are usually constructed using either collaborative filtering, content-based filtering, or both. The most traditional way of developing a collaborative filtering recommender system is using matrix factorization, which works by decomposing a user-item interaction matrix into the product of two lower dimensionality rectangular matrix. However, as new technologies appear, matrix factorization is often replaced by other algorithms that could perform better than in a recommendation system. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing. These successes are made possible by deep learning algorithms’ outstanding ability to learn feature representations non-linearly. The influence of deep learning is also prevalent in recommender systems, as demonstrated by its effectiveness when applied to information retrieval and recommender research. This research project performs an analysis and implementation on variants of two deep learning algorithms, autoencoder and restricted Boltzmann machines, and how they perform in recommender systems compared to matrix factorization
Leveraging Deep Learning Techniques on Collaborative Filtering Recommender Systems
With the exponentially increasing volume of online data, searching and
finding required information have become an extensive and time-consuming task.
Recommender Systems as a subclass of information retrieval and decision support
systems by providing personalized suggestions helping users access what they
need more efficiently. Among the different techniques for building a
recommender system, Collaborative Filtering (CF) is the most popular and
widespread approach. However, cold start and data sparsity are the fundamental
challenges ahead of implementing an effective CF-based recommender. Recent
successful developments in enhancing and implementing deep learning
architectures motivated many studies to propose deep learning-based solutions
for solving the recommenders' weak points. In this research, unlike the past
similar works about using deep learning architectures in recommender systems
that covered different techniques generally, we specifically provide a
comprehensive review of deep learning-based collaborative filtering recommender
systems. This in-depth filtering gives a clear overview of the level of
popularity, gaps, and ignored areas on leveraging deep learning techniques to
build CF-based systems as the most influential recommenders.Comment: 24 pages, 14 figure
Joint Deep Modeling of Users and Items Using Reviews for Recommendation
A large amount of information exists in reviews written by users. This source
of information has been ignored by most of the current recommender systems
while it can potentially alleviate the sparsity problem and improve the quality
of recommendations. In this paper, we present a deep model to learn item
properties and user behaviors jointly from review text. The proposed model,
named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel
neural networks coupled in the last layers. One of the networks focuses on
learning user behaviors exploiting reviews written by the user, and the other
one learns item properties from the reviews written for the item. A shared
layer is introduced on the top to couple these two networks together. The
shared layer enables latent factors learned for users and items to interact
with each other in a manner similar to factorization machine techniques.
Experimental results demonstrate that DeepCoNN significantly outperforms all
baseline recommender systems on a variety of datasets.Comment: WSDM 201
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