687 research outputs found
Attentive Aspect Modeling for Review-aware Recommendation
In recent years, many studies extract aspects from user reviews and integrate
them with ratings for improving the recommendation performance. The common
aspects mentioned in a user's reviews and a product's reviews indicate indirect
connections between the user and product. However, these aspect-based methods
suffer from two problems. First, the common aspects are usually very sparse,
which is caused by the sparsity of user-product interactions and the diversity
of individual users' vocabularies. Second, a user's interests on aspects could
be different with respect to different products, which are usually assumed to
be static in existing methods. In this paper, we propose an Attentive
Aspect-based Recommendation Model (AARM) to tackle these challenges. For the
first problem, to enrich the aspect connections between user and product,
besides common aspects, AARM also models the interactions between synonymous
and similar aspects. For the second problem, a neural attention network which
simultaneously considers user, product and aspect information is constructed to
capture a user's attention towards aspects when examining different products.
Extensive quantitative and qualitative experiments show that AARM can
effectively alleviate the two aforementioned problems and significantly
outperforms several state-of-the-art recommendation methods on top-N
recommendation task.Comment: Camera-ready manuscript for TOI
Frequency Enhanced Hybrid Attention Network for Sequential Recommendation
The self-attention mechanism, which equips with a strong capability of
modeling long-range dependencies, is one of the extensively used techniques in
the sequential recommendation field. However, many recent studies represent
that current self-attention based models are low-pass filters and are
inadequate to capture high-frequency information. Furthermore, since the items
in the user behaviors are intertwined with each other, these models are
incomplete to distinguish the inherent periodicity obscured in the time domain.
In this work, we shift the perspective to the frequency domain, and propose a
novel Frequency Enhanced Hybrid Attention Network for Sequential
Recommendation, namely FEARec. In this model, we firstly improve the original
time domain self-attention in the frequency domain with a ramp structure to
make both low-frequency and high-frequency information could be explicitly
learned in our approach. Moreover, we additionally design a similar attention
mechanism via auto-correlation in the frequency domain to capture the periodic
characteristics and fuse the time and frequency level attention in a union
model. Finally, both contrastive learning and frequency regularization are
utilized to ensure that multiple views are aligned in both the time domain and
frequency domain. Extensive experiments conducted on four widely used benchmark
datasets demonstrate that the proposed model performs significantly better than
the state-of-the-art approaches.Comment: 11 pages, 7 figures, The 46th International ACM SIGIR Conference on
Research and Development in Information Retrieva
Self-Supervised Multi-Modal Sequential Recommendation
With the increasing development of e-commerce and online services,
personalized recommendation systems have become crucial for enhancing user
satisfaction and driving business revenue. Traditional sequential
recommendation methods that rely on explicit item IDs encounter challenges in
handling item cold start and domain transfer problems. Recent approaches have
attempted to use modal features associated with items as a replacement for item
IDs, enabling the transfer of learned knowledge across different datasets.
However, these methods typically calculate the correlation between the model's
output and item embeddings, which may suffer from inconsistencies between
high-level feature vectors and low-level feature embeddings, thereby hindering
further model learning. To address this issue, we propose a dual-tower
retrieval architecture for sequence recommendation. In this architecture, the
predicted embedding from the user encoder is used to retrieve the generated
embedding from the item encoder, thereby alleviating the issue of inconsistent
feature levels. Moreover, in order to further improve the retrieval performance
of the model, we also propose a self-supervised multi-modal pretraining method
inspired by the consistency property of contrastive learning. This pretraining
method enables the model to align various feature combinations of items,
thereby effectively generalizing to diverse datasets with different item
features. We evaluate the proposed method on five publicly available datasets
and conduct extensive experiments. The results demonstrate significant
performance improvement of our method
Mutual Wasserstein Discrepancy Minimization for Sequential Recommendation
Self-supervised sequential recommendation significantly improves
recommendation performance by maximizing mutual information with well-designed
data augmentations. However, the mutual information estimation is based on the
calculation of Kullback Leibler divergence with several limitations, including
asymmetrical estimation, the exponential need of the sample size, and training
instability. Also, existing data augmentations are mostly stochastic and can
potentially break sequential correlations with random modifications. These two
issues motivate us to investigate an alternative robust mutual information
measurement capable of modeling uncertainty and alleviating KL divergence
limitations. To this end, we propose a novel self-supervised learning framework
based on Mutual WasserStein discrepancy minimization MStein for the sequential
recommendation. We propose the Wasserstein Discrepancy Measurement to measure
the mutual information between augmented sequences. Wasserstein Discrepancy
Measurement builds upon the 2-Wasserstein distance, which is more robust, more
efficient in small batch sizes, and able to model the uncertainty of stochastic
augmentation processes. We also propose a novel contrastive learning loss based
on Wasserstein Discrepancy Measurement. Extensive experiments on four benchmark
datasets demonstrate the effectiveness of MStein over baselines. More
quantitative analyses show the robustness against perturbations and training
efficiency in batch size. Finally, improvements analysis indicates better
representations of popular users or items with significant uncertainty. The
source code is at https://github.com/zfan20/MStein.Comment: Updated with the correction of the asymmetric mistake on the mutual
information connectio
DiffuRec: A Diffusion Model for Sequential Recommendation
Mainstream solutions to Sequential Recommendation (SR) represent items with
fixed vectors. These vectors have limited capability in capturing items' latent
aspects and users' diverse preferences. As a new generative paradigm, Diffusion
models have achieved excellent performance in areas like computer vision and
natural language processing. To our understanding, its unique merit in
representation generation well fits the problem setting of sequential
recommendation. In this paper, we make the very first attempt to adapt
Diffusion model to SR and propose DiffuRec, for item representation
construction and uncertainty injection. Rather than modeling item
representations as fixed vectors, we represent them as distributions in
DiffuRec, which reflect user's multiple interests and item's various aspects
adaptively. In diffusion phase, DiffuRec corrupts the target item embedding
into a Gaussian distribution via noise adding, which is further applied for
sequential item distribution representation generation and uncertainty
injection. Afterwards, the item representation is fed into an Approximator for
target item representation reconstruction. In reversion phase, based on user's
historical interaction behaviors, we reverse a Gaussian noise into the target
item representation, then apply rounding operation for target item prediction.
Experiments over four datasets show that DiffuRec outperforms strong baselines
by a large margin
A Comprehensive Survey on Generative Diffusion Models for Structured Data
In recent years, generative diffusion models have achieved a rapid paradigm
shift in deep generative models by showing groundbreaking performance across
various applications. Meanwhile, structured data, encompassing tabular and time
series data, has been received comparatively limited attention from the deep
learning research community, despite its omnipresence and extensive
applications. Thus, there is still a lack of literature and its reviews on
structured data modelling via diffusion models, compared to other data
modalities such as visual and textual data. To address this gap, we present a
comprehensive review of recently proposed diffusion models in the field of
structured data. First, this survey provides a concise overview of the
score-based diffusion model theory, subsequently proceeding to the technical
descriptions of the majority of pioneering works that used structured data in
both data-driven general tasks and domain-specific applications. Thereafter, we
analyse and discuss the limitations and challenges shown in existing works and
suggest potential research directions. We hope this review serves as a catalyst
for the research community, promoting developments in generative diffusion
models for structured data.Comment: 20 pages, 1 figure, 2 table
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