2,962 research outputs found
Mixed Information Flow for Cross-domain Sequential Recommendations
Cross-domain sequential recommendation is the task of predict the next item
that the user is most likely to interact with based on past sequential behavior
from multiple domains. One of the key challenges in cross-domain sequential
recommendation is to grasp and transfer the flow of information from multiple
domains so as to promote recommendations in all domains. Previous studies have
investigated the flow of behavioral information by exploring the connection
between items from different domains. The flow of knowledge (i.e., the
connection between knowledge from different domains) has so far been neglected.
In this paper, we propose a mixed information flow network for cross-domain
sequential recommendation to consider both the flow of behavioral information
and the flow of knowledge by incorporating a behavior transfer unit and a
knowledge transfer unit. The proposed mixed information flow network is able to
decide when cross-domain information should be used and, if so, which
cross-domain information should be used to enrich the sequence representation
according to users' current preferences. Extensive experiments conducted on
four e-commerce datasets demonstrate that mixed information flow network is
able to further improve recommendation performance in different domains by
modeling mixed information flow.Comment: 26 pages, 6 figures, TKDD journal, 7 co-author
Time Interval-enhanced Graph Neural Network for Shared-account Cross-domain Sequential Recommendation
Shared-account Cross-domain Sequential Recommendation (SCSR) task aims to
recommend the next item via leveraging the mixed user behaviors in multiple
domains. It is gaining immense research attention as more and more users tend
to sign up on different platforms and share accounts with others to access
domain-specific services. Existing works on SCSR mainly rely on mining
sequential patterns via Recurrent Neural Network (RNN)-based models, which
suffer from the following limitations: 1) RNN-based methods overwhelmingly
target discovering sequential dependencies in single-user behaviors. They are
not expressive enough to capture the relationships among multiple entities in
SCSR. 2) All existing methods bridge two domains via knowledge transfer in the
latent space, and ignore the explicit cross-domain graph structure. 3) None
existing studies consider the time interval information among items, which is
essential in the sequential recommendation for characterizing different items
and learning discriminative representations for them. In this work, we propose
a new graph-based solution, namely TiDA-GCN, to address the above challenges.
Specifically, we first link users and items in each domain as a graph. Then, we
devise a domain-aware graph convolution network to learn userspecific node
representations. To fully account for users' domainspecific preferences on
items, two effective attention mechanisms are further developed to selectively
guide the message passing process. Moreover, to further enhance item- and
account-level representation learning, we incorporate the time interval into
the message passing, and design an account-aware self-attention module for
learning items' interactive characteristics. Experiments demonstrate the
superiority of our proposed method from various aspects.Comment: 15 pages, 6 figure
Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph
Knowledge graphs (KGs) are commonly used as side information to enhance
collaborative signals and improve recommendation quality. In the context of
knowledge-aware recommendation (KGR), graph neural networks (GNNs) have emerged
as promising solutions for modeling factual and semantic information in KGs.
However, the long-tail distribution of entities leads to sparsity in
supervision signals, which weakens the quality of item representation when
utilizing KG enhancement. Additionally, the binary relation representation of
KGs simplifies hyper-relational facts, making it challenging to model complex
real-world information. Furthermore, the over-smoothing phenomenon results in
indistinguishable representations and information loss. To address these
challenges, we propose the SDK (Self-Supervised Dynamic Hypergraph
Recommendation based on Hyper-Relational Knowledge Graph) framework. This
framework establishes a cross-view hypergraph self-supervised learning
mechanism for KG enhancement. Specifically, we model hyper-relational facts in
KGs to capture interdependencies between entities under complete semantic
conditions. With the refined representation, a hypergraph is dynamically
constructed to preserve features in the deep vector space, thereby alleviating
the over-smoothing problem. Furthermore, we mine external supervision signals
from both the global perspective of the hypergraph and the local perspective of
collaborative filtering (CF) to guide the model prediction process. Extensive
experiments conducted on different datasets demonstrate the superiority of the
SDK framework over state-of-the-art models. The results showcase its ability to
alleviate the effects of over-smoothing and supervision signal sparsity
Recommendation Systems: An Insight Into Current Development and Future Research Challenges
Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets
Dual Preference Distribution Learning for Item Recommendation
Recommender systems can automatically recommend users with items that they
probably like. The goal of them is to model the user-item interaction by
effectively representing the users and items. Existing methods have primarily
learned the user's preferences and item's features with vectorized embeddings,
and modeled the user's general preferences to items by the interaction of them.
In fact, users have their specific preferences to item attributes and different
preferences are usually related. Therefore, exploring the fine-grained
preferences as well as modeling the relationships among user's different
preferences could improve the recommendation performance. Toward this end, we
propose a dual preference distribution learning framework (DUPLE), which aims
to jointly learn a general preference distribution and a specific preference
distribution for a given user, where the former corresponds to the user's
general preference to items and the latter refers to the user's specific
preference to item attributes. Notably, the mean vector of each Gaussian
distribution can capture the user's preferences, and the covariance matrix can
learn their relationship. Moreover, we can summarize a preferred attribute
profile for each user, depicting his/her preferred item attributes. We then can
provide the explanation for each recommended item by checking the overlap
between its attributes and the user's preferred attribute profile. Extensive
quantitative and qualitative experiments on six public datasets demonstrate the
effectiveness and explainability of the DUPLE method.Comment: 23 pages, 7 figures. This manuscript has been accepted by ACM
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