174 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 Survey on Bayesian Deep Learning
A comprehensive artificial intelligence system needs to not only perceive the
environment with different `senses' (e.g., seeing and hearing) but also infer
the world's conditional (or even causal) relations and corresponding
uncertainty. The past decade has seen major advances in many perception tasks
such as visual object recognition and speech recognition using deep learning
models. For higher-level inference, however, probabilistic graphical models
with their Bayesian nature are still more powerful and flexible. In recent
years, Bayesian deep learning has emerged as a unified probabilistic framework
to tightly integrate deep learning and Bayesian models. In this general
framework, the perception of text or images using deep learning can boost the
performance of higher-level inference and in turn, the feedback from the
inference process is able to enhance the perception of text or images. This
survey provides a comprehensive introduction to Bayesian deep learning and
reviews its recent applications on recommender systems, topic models, control,
etc. Besides, we also discuss the relationship and differences between Bayesian
deep learning and other related topics such as Bayesian treatment of neural
networks.Comment: To appear in ACM Computing Surveys (CSUR) 202
AutoSVD++: An Efficient Hybrid Collaborative Filtering Model via Contractive Auto-encoders
Collaborative filtering (CF) has been successfully used to provide users with
personalized products and services. However, dealing with the increasing
sparseness of user-item matrix still remains a challenge. To tackle such issue,
hybrid CF such as combining with content based filtering and leveraging side
information of users and items has been extensively studied to enhance
performance. However, most of these approaches depend on hand-crafted feature
engineering, which are usually noise-prone and biased by different feature
extraction and selection schemes. In this paper, we propose a new hybrid model
by generalizing contractive auto-encoder paradigm into matrix factorization
framework with good scalability and computational efficiency, which jointly
model content information as representations of effectiveness and compactness,
and leverage implicit user feedback to make accurate recommendations. Extensive
experiments conducted over three large scale real datasets indicate the
proposed approach outperforms the compared methods for item recommendation.Comment: 4 pages, 3 figure
Neural Ideal Point Estimation Network
Understanding politics is challenging because the politics take the influence
from everything. Even we limit ourselves to the political context in the
legislative processes; we need a better understanding of latent factors, such
as legislators, bills, their ideal points, and their relations. From the
modeling perspective, this is difficult 1) because these observations lie in a
high dimension that requires learning on low dimensional representations, and
2) because these observations require complex probabilistic modeling with
latent variables to reflect the causalities. This paper presents a new model to
reflect and understand this political setting, NIPEN, including factors
mentioned above in the legislation. We propose two versions of NIPEN: one is a
hybrid model of deep learning and probabilistic graphical model, and the other
model is a neural tensor model. Our result indicates that NIPEN successfully
learns the manifold of the legislative bill texts, and NIPEN utilizes the
learned low-dimensional latent variables to increase the prediction performance
of legislators' votings. Additionally, by virtue of being a domain-rich
probabilistic model, NIPEN shows the hidden strength of the legislators' trust
network and their various characteristics on casting votes
A Collective Variational Autoencoder for Top- Recommendation with Side Information
Recommender systems have been studied extensively due to their practical use
in many real-world scenarios. Despite this, generating effective
recommendations with sparse user ratings remains a challenge. Side information
associated with items has been widely utilized to address rating sparsity.
Existing recommendation models that use side information are linear and, hence,
have restricted expressiveness. Deep learning has been used to capture
non-linearities by learning deep item representations from side information but
as side information is high-dimensional existing deep models tend to have large
input dimensionality, which dominates their overall size. This makes them
difficult to train, especially with small numbers of inputs.
Rather than learning item representations, which is problematic with
high-dimensional side information, in this paper, we propose to learn feature
representation through deep learning from side information. Learning feature
representations, on the other hand, ensures a sufficient number of inputs to
train a deep network. To achieve this, we propose to simultaneously recover
user ratings and side information, by using a Variational Autoencoder (VAE).
Specifically, user ratings and side information are encoded and decoded
collectively through the same inference network and generation network. This is
possible as both user ratings and side information are data associated with
items. To account for the heterogeneity of user rating and side information,
the final layer of the generation network follows different distributions
depending on the type of information. The proposed model is easy to implement
and efficient to optimize and is shown to outperform state-of-the-art top-
recommendation methods that use side information.Comment: 7 pages, 3 figures, DLRS workshop 201
A deep learning-based hybrid model for recommendation generation and ranking
A recommender system plays a vital role in information filtering and retrieval, and its application is omnipresent in many domains. There are some drawbacks such as the cold-start and the data sparsity problems which affect the performance of the recommender model. Various studies help with drastically improving the performance of recommender systems via unique methods, such as the traditional way of performing matrix factorization (MF) and also applying deep learning (DL) techniques in recent years. By using DL in the recommender system, we can overcome the difficulties of collaborative filtering. DL now focuses mainly on modeling content descriptions, but those models ignore the main factor of user–item interaction. In the proposed hybrid Bayesian stacked auto-denoising encoder (HBSADE) model, it recognizes the latent interests of the user and analyzes contextual reviews that are performed through the MF method. The objective of the model is to identify the user’s point of interest, recommending products/services based on the user’s latent interests. The proposed two-stage novel hybrid deep learning-based collaborative filtering method explores the user’s point of interest, captures the communications between items and users and provides better recommendations in a personalized way. We used a multilayer neural network to manipulate the nonlinearities between the user and item communication from data. Experiments were to prove that our HBSADE outperforms existing methodologies over Amazon-b and Book-Crossing datasets
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