209 research outputs found
Signed Distance-based Deep Memory Recommender
Personalized recommendation algorithms learn a user's preference for an item
by measuring a distance/similarity between them. However, some of the existing
recommendation models (e.g., matrix factorization) assume a linear relationship
between the user and item. This approach limits the capacity of recommender
systems, since the interactions between users and items in real-world
applications are much more complex than the linear relationship. To overcome
this limitation, in this paper, we design and propose a deep learning framework
called Signed Distance-based Deep Memory Recommender, which captures non-linear
relationships between users and items explicitly and implicitly, and work well
in both general recommendation task and shopping basket-based recommendation
task. Through an extensive empirical study on six real-world datasets in the
two recommendation tasks, our proposed approach achieved significant
improvement over ten state-of-the-art recommendation models
Dual-Ganularity Contrastive Learning for Session-based Recommendation
Session-based recommendation systems(SBRS) are more suitable for the current
e-commerce and streaming media recommendation scenarios and thus have become a
hot topic. The data encountered by SBRS is typically highly sparse, which also
serves as one of the bottlenecks limiting the accuracy of recommendations. So
Contrastive Learning(CL) is applied in SBRS owing to its capability of
improving embedding learning under the condition of sparse data. However,
existing CL strategies are limited in their ability to enforce finer-grained
(e.g., factor-level) comparisons and, as a result, are unable to capture subtle
differences between instances. More than that, these strategies usually use
item or segment dropout as a means of data augmentation which may result in
sparser data and thus ineffective self-supervised signals. By addressing the
two aforementioned limitations, we introduce a novel multi-granularity CL
framework. Specifically, two extra augmented embedding convolution channels
with different granularities are constructed and the embeddings learned by them
are compared with those learned from original view to complete the CL tasks. At
factor-level, we employ Disentangled Representation Learning to obtain
finer-grained data(e.g. factor-level embeddings), with which we can construct
factor-level convolution channels. At item-level, the star graph is deployed as
the augmented data and graph convolution on it can ensure the effectiveness of
self-supervised signals. Compare the learned embeddings of these two views with
the learned embeddings of the basic view to achieve CL at two granularities.
Finally, the more precise item-level and factor-level embeddings obtained are
referenced to generate personalized recommendations for the user. The proposed
model is validated through extensive experiments on two benchmark datasets,
showcasing superior performance compared to existing methods
Automated Prompting for Non-overlapping Cross-domain Sequential Recommendation
Cross-domain Recommendation (CR) has been extensively studied in recent years
to alleviate the data sparsity issue in recommender systems by utilizing
different domain information. In this work, we focus on the more general
Non-overlapping Cross-domain Sequential Recommendation (NCSR) scenario. NCSR is
challenging because there are no overlapped entities (e.g., users and items)
between domains, and there is only users' implicit feedback and no content
information. Previous CR methods cannot solve NCSR well, since (1) they either
need extra content to align domains or need explicit domain alignment
constraints to reduce the domain discrepancy from domain-invariant features,
(2) they pay more attention to users' explicit feedback (i.e., users' rating
data) and cannot well capture their sequential interaction patterns, (3) they
usually do a single-target cross-domain recommendation task and seldom
investigate the dual-target ones. Considering the above challenges, we propose
Prompt Learning-based Cross-domain Recommender (PLCR), an automated
prompting-based recommendation framework for the NCSR task. Specifically, to
address the challenge (1), PLCR resorts to learning domain-invariant and
domain-specific representations via its prompt learning component, where the
domain alignment constraint is discarded. For challenges (2) and (3), PLCR
introduces a pre-trained sequence encoder to learn users' sequential
interaction patterns, and conducts a dual-learning target with a separation
constraint to enhance recommendations in both domains. Our empirical study on
two sub-collections of Amazon demonstrates the advance of PLCR compared with
some related SOTA methods
Equivariant Contrastive Learning for Sequential Recommendation
Contrastive learning (CL) benefits the training of sequential recommendation
models with informative self-supervision signals. Existing solutions apply
general sequential data augmentation strategies to generate positive pairs and
encourage their representations to be invariant. However, due to the inherent
properties of user behavior sequences, some augmentation strategies, such as
item substitution, can lead to changes in user intent. Learning
indiscriminately invariant representations for all augmentation strategies
might be suboptimal. Therefore, we propose Equivariant Contrastive Learning for
Sequential Recommendation (ECL-SR), which endows SR models with great
discriminative power, making the learned user behavior representations
sensitive to invasive augmentations (e.g., item substitution) and insensitive
to mild augmentations (e.g., featurelevel dropout masking). In detail, we use
the conditional discriminator to capture differences in behavior due to item
substitution, which encourages the user behavior encoder to be equivariant to
invasive augmentations. Comprehensive experiments on four benchmark datasets
show that the proposed ECL-SR framework achieves competitive performance
compared to state-of-the-art SR models. The source code is available at
https://github.com/Tokkiu/ECL.Comment: Accepted by RecSys 202
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