1,686 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
Adversarial Variational Embedding for Robust Semi-supervised Learning
Semi-supervised learning is sought for leveraging the unlabelled data when
labelled data is difficult or expensive to acquire. Deep generative models
(e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial
Networks (GANs) have recently shown promising performance in semi-supervised
classification for the excellent discriminative representing ability. However,
the latent code learned by the traditional VAE is not exclusive (repeatable)
for a specific input sample, which prevents it from excellent classification
performance. In particular, the learned latent representation depends on a
non-exclusive component which is stochastically sampled from the prior
distribution. Moreover, the semi-supervised GAN models generate data from
pre-defined distribution (e.g., Gaussian noises) which is independent of the
input data distribution and may obstruct the convergence and is difficult to
control the distribution of the generated data. To address the aforementioned
issues, we propose a novel Adversarial Variational Embedding (AVAE) framework
for robust and effective semi-supervised learning to leverage both the
advantage of GAN as a high quality generative model and VAE as a posterior
distribution learner. The proposed approach first produces an exclusive latent
code by the model which we call VAE++, and meanwhile, provides a meaningful
prior distribution for the generator of GAN. The proposed approach is evaluated
over four different real-world applications and we show that our method
outperforms the state-of-the-art models, which confirms that the combination of
VAE++ and GAN can provide significant improvements in semisupervised
classification.Comment: 9 pages, Accepted by Research Track in KDD 201
A Long-Tail Friendly Representation Framework for Artist and Music Similarity
The investigation of the similarity between artists and music is crucial in
music retrieval and recommendation, and addressing the challenge of the
long-tail phenomenon is increasingly important. This paper proposes a Long-Tail
Friendly Representation Framework (LTFRF) that utilizes neural networks to
model the similarity relationship. Our approach integrates music, user,
metadata, and relationship data into a unified metric learning framework, and
employs a meta-consistency relationship as a regular term to introduce the
Multi-Relationship Loss. Compared to the Graph Neural Network (GNN), our
proposed framework improves the representation performance in long-tail
scenarios, which are characterized by sparse relationships between artists and
music. We conduct experiments and analysis on the AllMusic dataset, and the
results demonstrate that our framework provides a favorable generalization of
artist and music representation. Specifically, on similar artist/music
recommendation tasks, the LTFRF outperforms the baseline by 9.69%/19.42% in Hit
Ratio@10, and in long-tail cases, the framework achieves 11.05%/14.14% higher
than the baseline in Consistent@10
Graph Collaborative Signals Denoising and Augmentation for Recommendation
Graph collaborative filtering (GCF) is a popular technique for capturing
high-order collaborative signals in recommendation systems. However, GCF's
bipartite adjacency matrix, which defines the neighbors being aggregated based
on user-item interactions, can be noisy for users/items with abundant
interactions and insufficient for users/items with scarce interactions.
Additionally, the adjacency matrix ignores user-user and item-item
correlations, which can limit the scope of beneficial neighbors being
aggregated.
In this work, we propose a new graph adjacency matrix that incorporates
user-user and item-item correlations, as well as a properly designed user-item
interaction matrix that balances the number of interactions across all users.
To achieve this, we pre-train a graph-based recommendation method to obtain
users/items embeddings, and then enhance the user-item interaction matrix via
top-K sampling. We also augment the symmetric user-user and item-item
correlation components to the adjacency matrix. Our experiments demonstrate
that the enhanced user-item interaction matrix with improved neighbors and
lower density leads to significant benefits in graph-based recommendation.
Moreover, we show that the inclusion of user-user and item-item correlations
can improve recommendations for users with both abundant and insufficient
interactions. The code is in \url{https://github.com/zfan20/GraphDA}.Comment: Short Paper Accepted by SIGIR 2023, 6 page
CASPR: Customer Activity Sequence-based Prediction and Representation
Tasks critical to enterprise profitability, such as customer churn
prediction, fraudulent account detection or customer lifetime value estimation,
are often tackled by models trained on features engineered from customer data
in tabular format. Application-specific feature engineering adds development,
operationalization and maintenance costs over time. Recent advances in
representation learning present an opportunity to simplify and generalize
feature engineering across applications. When applying these advancements to
tabular data researchers deal with data heterogeneity, variations in customer
engagement history or the sheer volume of enterprise datasets. In this paper,
we propose a novel approach to encode tabular data containing customer
transactions, purchase history and other interactions into a generic
representation of a customer's association with the business. We then evaluate
these embeddings as features to train multiple models spanning a variety of
applications. CASPR, Customer Activity Sequence-based Prediction and
Representation, applies Transformer architecture to encode activity sequences
to improve model performance and avoid bespoke feature engineering across
applications. Our experiments at scale validate CASPR for both small and large
enterprise applications.Comment: Presented at the Table Representation Learning Workshop, NeurIPS
2022, New Orleans. Authors listed in random orde
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