1,013 research outputs found
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
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
Evidence Transfer for Improving Clustering Tasks Using External Categorical Evidence
In this paper we introduce evidence transfer for clustering, a deep learning
method that can incrementally manipulate the latent representations of an
autoencoder, according to external categorical evidence, in order to improve a
clustering outcome. By evidence transfer we define the process by which the
categorical outcome of an external, auxiliary task is exploited to improve a
primary task, in this case representation learning for clustering. Our proposed
method makes no assumptions regarding the categorical evidence presented, nor
the structure of the latent space. We compare our method, against the baseline
solution by performing k-means clustering before and after its deployment.
Experiments with three different kinds of evidence show that our method
effectively manipulates the latent representations when introduced with real
corresponding evidence, while remaining robust when presented with low quality
evidence
LRMM: Learning to Recommend with Missing Modalities
Multimodal learning has shown promising performance in content-based
recommendation due to the auxiliary user and item information of multiple
modalities such as text and images. However, the problem of incomplete and
missing modality is rarely explored and most existing methods fail in learning
a recommendation model with missing or corrupted modalities. In this paper, we
propose LRMM, a novel framework that mitigates not only the problem of missing
modalities but also more generally the cold-start problem of recommender
systems. We propose modality dropout (m-drop) and a multimodal sequential
autoencoder (m-auto) to learn multimodal representations for complementing and
imputing missing modalities. Extensive experiments on real-world Amazon data
show that LRMM achieves state-of-the-art performance on rating prediction
tasks. More importantly, LRMM is more robust to previous methods in alleviating
data-sparsity and the cold-start problem.Comment: 11 pages, EMNLP 201
Machine Learning Models for Context-Aware Recommender Systems
The mass adoption of the internet has resulted in the exponential growth of products and services on the world wide web. An individual consumer, faced with this data deluge, is expected to make reasonable choices saving time and money. Organizations are facing increased competition, and they are looking for innovative ways to increase revenue and customer loyalty. A business wants to target the right product or service to an individual consumer, and this drives personalized recommendation. Recommender systems, designed to provide personalized recommendations, initially focused only on the user-item interaction. However, these systems evolved to provide a context-aware recommendations. Context-aware recommender systems utilize additional context, such as genre for movie recommendation, while recommending items to users. Latent factor methods have been a popular choice for recommender systems. With the resurgence of neural networks, there has also been a trend towards applying deep learning methods to recommender systems.
This study proposes a novel contextual latent factor model that is capable of utilizing the context from a dual-perspective of both users and items. The proposed model, known as the Group-Aware Latent Factor Model (GLFM), is applied to the event recommendation task. The GLFM model is extensible, and it allows other contextual attributes to be easily be incorporated into the model. While latent-factor models have been extremely popular for recommender systems, they are unable to model the complex non-linear user-item relationships. This has resulted in the interest in applying deep learning methods to recommender systems. This study also proposes another novel method based on the denoising autoencoder architecture, which is referred to as the Attentive Contextual Denoising Autoencoder (ACDA). The ACDA model augments the basic denoising autoencoder with a context-driven attention mechanism to provide personalized recommendation. The ACDA model is applied to the event and movie recommendation tasks.
The effectiveness of the proposed models is demonstrated against real-world datasets from Meetup and Movielens, and the results are compared against the current state-of-the-art baseline methods
One for All, All for One: Learning and Transferring User Embeddings for Cross-Domain Recommendation
Cross-domain recommendation is an important method to improve recommender
system performance, especially when observations in target domains are sparse.
However, most existing techniques focus on single-target or dual-target
cross-domain recommendation (CDR) and are hard to be generalized to CDR with
multiple target domains. In addition, the negative transfer problem is
prevalent in CDR, where the recommendation performance in a target domain may
not always be enhanced by knowledge learned from a source domain, especially
when the source domain has sparse data. In this study, we propose CAT-ART, a
multi-target CDR method that learns to improve recommendations in all
participating domains through representation learning and embedding transfer.
Our method consists of two parts: a self-supervised Contrastive AuToencoder
(CAT) framework to generate global user embeddings based on information from
all participating domains, and an Attention-based Representation Transfer (ART)
framework which transfers domain-specific user embeddings from other domains to
assist with target domain recommendation. CAT-ART boosts the recommendation
performance in any target domain through the combined use of the learned global
user representation and knowledge transferred from other domains, in addition
to the original user embedding in the target domain. We conducted extensive
experiments on a collected real-world CDR dataset spanning 5 domains and
involving a million users. Experimental results demonstrate the superiority of
the proposed method over a range of prior arts. We further conducted ablation
studies to verify the effectiveness of the proposed components. Our collected
dataset will be open-sourced to facilitate future research in the field of
multi-domain recommender systems and user modeling.Comment: 9 pages, accepted by WSDM 202
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