3,482 research outputs found
Unbiased and Robust: External Attention-enhanced Graph Contrastive Learning for Cross-domain Sequential Recommendation
Cross-domain sequential recommenders (CSRs) are gaining considerable research
attention as they can capture user sequential preference by leveraging side
information from multiple domains. However, these works typically follow an
ideal setup, i.e., different domains obey similar data distribution, which
ignores the bias brought by asymmetric interaction densities (a.k.a. the
inter-domain density bias). Besides, the frequently adopted mechanism (e.g.,
the self-attention network) in sequence encoder only focuses on the
interactions within a local view, which overlooks the global correlations
between different training batches. To this end, we propose an External
Attention-enhanced Graph Contrastive Learning framework, namely EA-GCL.
Specifically, to remove the impact of the inter-domain density bias, an
auxiliary Self-Supervised Learning (SSL) task is attached to the traditional
graph encoder under a multi-task learning manner. To robustly capture users'
behavioral patterns, we develop an external attention-based sequence encoder
that contains an MLP-based memory-sharing structure. Unlike the self-attention
mechanism, such a structure can effectively alleviate the bias interference
from the batch-based training scheme. Extensive experiments on two real-world
datasets demonstrate that EA-GCL outperforms several state-of-the-art baselines
on CSR tasks. The source codes and relevant datasets are available at
https://github.com/HoupingY/EA-GCL.Comment: 9 pages, 4 figures, accepted by ICDM 2023 (workshop-GML4Rec
Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction
User response prediction, which models the user preference w.r.t. the
presented items, plays a key role in online services. With two-decade rapid
development, nowadays the cumulated user behavior sequences on mature Internet
service platforms have become extremely long since the user's first
registration. Each user not only has intrinsic tastes, but also keeps changing
her personal interests during lifetime. Hence, it is challenging to handle such
lifelong sequential modeling for each individual user. Existing methodologies
for sequential modeling are only capable of dealing with relatively recent user
behaviors, which leaves huge space for modeling long-term especially lifelong
sequential patterns to facilitate user modeling. Moreover, one user's behavior
may be accounted for various previous behaviors within her whole online
activity history, i.e., long-term dependency with multi-scale sequential
patterns. In order to tackle these challenges, in this paper, we propose a
Hierarchical Periodic Memory Network for lifelong sequential modeling with
personalized memorization of sequential patterns for each user. The model also
adopts a hierarchical and periodical updating mechanism to capture multi-scale
sequential patterns of user interests while supporting the evolving user
behavior logs. The experimental results over three large-scale real-world
datasets have demonstrated the advantages of our proposed model with
significant improvement in user response prediction performance against the
state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets:
https://github.com/alimamarankgroup/HPM
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
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
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