253 research outputs found
Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation
Recently, recommender systems have been able to emit substantially improved
recommendations by leveraging user-provided reviews. Existing methods typically
merge all reviews of a given user or item into a long document, and then
process user and item documents in the same manner. In practice, however, these
two sets of reviews are notably different: users' reviews reflect a variety of
items that they have bought and are hence very heterogeneous in their topics,
while an item's reviews pertain only to that single item and are thus topically
homogeneous. In this work, we develop a novel neural network model that
properly accounts for this important difference by means of asymmetric
attentive modules. The user module learns to attend to only those signals that
are relevant with respect to the target item, whereas the item module learns to
extract the most salient contents with regard to properties of the item. Our
multi-hierarchical paradigm accounts for the fact that neither are all reviews
equally useful, nor are all sentences within each review equally pertinent.
Extensive experimental results on a variety of real datasets demonstrate the
effectiveness of our method
Review-Based Domain Disentanglement without Duplicate Users or Contexts for Cross-Domain Recommendation
A cross-domain recommendation has shown promising results in solving
data-sparsity and cold-start problems. Despite such progress, existing methods
focus on domain-shareable information (overlapped users or same contexts) for a
knowledge transfer, and they fail to generalize well without such requirements.
To deal with these problems, we suggest utilizing review texts that are general
to most e-commerce systems. Our model (named SER) uses three text analysis
modules, guided by a single domain discriminator for disentangled
representation learning. Here, we suggest a novel optimization strategy that
can enhance the quality of domain disentanglement, and also debilitates
detrimental information of a source domain. Also, we extend the encoding
network from a single to multiple domains, which has proven to be powerful for
review-based recommender systems. Extensive experiments and ablation studies
demonstrate that our method is efficient, robust, and scalable compared to the
state-of-the-art single and cross-domain recommendation methods
Explainability in Music Recommender Systems
The most common way to listen to recorded music nowadays is via streaming
platforms which provide access to tens of millions of tracks. To assist users
in effectively browsing these large catalogs, the integration of Music
Recommender Systems (MRSs) has become essential. Current real-world MRSs are
often quite complex and optimized for recommendation accuracy. They combine
several building blocks based on collaborative filtering and content-based
recommendation. This complexity can hinder the ability to explain
recommendations to end users, which is particularly important for
recommendations perceived as unexpected or inappropriate. While pure
recommendation performance often correlates with user satisfaction,
explainability has a positive impact on other factors such as trust and
forgiveness, which are ultimately essential to maintain user loyalty.
In this article, we discuss how explainability can be addressed in the
context of MRSs. We provide perspectives on how explainability could improve
music recommendation algorithms and enhance user experience. First, we review
common dimensions and goals of recommenders' explainability and in general of
eXplainable Artificial Intelligence (XAI), and elaborate on the extent to which
these apply -- or need to be adapted -- to the specific characteristics of
music consumption and recommendation. Then, we show how explainability
components can be integrated within a MRS and in what form explanations can be
provided. Since the evaluation of explanation quality is decoupled from pure
accuracy-based evaluation criteria, we also discuss requirements and strategies
for evaluating explanations of music recommendations. Finally, we describe the
current challenges for introducing explainability within a large-scale
industrial music recommender system and provide research perspectives.Comment: To appear in AI Magazine, Special Topic on Recommender Systems 202
A Comprehensive Survey on Deep Graph Representation Learning
Graph representation learning aims to effectively encode high-dimensional
sparse graph-structured data into low-dimensional dense vectors, which is a
fundamental task that has been widely studied in a range of fields, including
machine learning and data mining. Classic graph embedding methods follow the
basic idea that the embedding vectors of interconnected nodes in the graph can
still maintain a relatively close distance, thereby preserving the structural
information between the nodes in the graph. However, this is sub-optimal due
to: (i) traditional methods have limited model capacity which limits the
learning performance; (ii) existing techniques typically rely on unsupervised
learning strategies and fail to couple with the latest learning paradigms;
(iii) representation learning and downstream tasks are dependent on each other
which should be jointly enhanced. With the remarkable success of deep learning,
deep graph representation learning has shown great potential and advantages
over shallow (traditional) methods, there exist a large number of deep graph
representation learning techniques have been proposed in the past decade,
especially graph neural networks. In this survey, we conduct a comprehensive
survey on current deep graph representation learning algorithms by proposing a
new taxonomy of existing state-of-the-art literature. Specifically, we
systematically summarize the essential components of graph representation
learning and categorize existing approaches by the ways of graph neural network
architectures and the most recent advanced learning paradigms. Moreover, this
survey also provides the practical and promising applications of deep graph
representation learning. Last but not least, we state new perspectives and
suggest challenging directions which deserve further investigations in the
future
Shilling Black-box Review-based Recommender Systems through Fake Review Generation
Review-Based Recommender Systems (RBRS) have attracted increasing research
interest due to their ability to alleviate well-known cold-start problems. RBRS
utilizes reviews to construct the user and items representations. However, in
this paper, we argue that such a reliance on reviews may instead expose systems
to the risk of being shilled. To explore this possibility, in this paper, we
propose the first generation-based model for shilling attacks against RBRSs.
Specifically, we learn a fake review generator through reinforcement learning,
which maliciously promotes items by forcing prediction shifts after adding
generated reviews to the system. By introducing the auxiliary rewards to
increase text fluency and diversity with the aid of pre-trained language models
and aspect predictors, the generated reviews can be effective for shilling with
high fidelity. Experimental results demonstrate that the proposed framework can
successfully attack three different kinds of RBRSs on the Amazon corpus with
three domains and Yelp corpus. Furthermore, human studies also show that the
generated reviews are fluent and informative. Finally, equipped with Attack
Review Generators (ARGs), RBRSs with adversarial training are much more robust
to malicious reviews
Recent Developments in Recommender Systems: A Survey
In this technical survey, we comprehensively summarize the latest
advancements in the field of recommender systems. The objective of this study
is to provide an overview of the current state-of-the-art in the field and
highlight the latest trends in the development of recommender systems. The
study starts with a comprehensive summary of the main taxonomy of recommender
systems, including personalized and group recommender systems, and then delves
into the category of knowledge-based recommender systems. In addition, the
survey analyzes the robustness, data bias, and fairness issues in recommender
systems, summarizing the evaluation metrics used to assess the performance of
these systems. Finally, the study provides insights into the latest trends in
the development of recommender systems and highlights the new directions for
future research in the field
Detection of Power Line Supporting Towers via Interpretable Semantic Segmentation of 3D Point Clouds
The inspection and maintenance of energy transmission networks are demanding and
crucial tasks for any transmission system operator. They rely on a combination of on-theground
staff and costly low-flying helicopters to visually inspect the power grid structure.
Recently, LiDAR-based inspections have shown the potential to accelerate and increase
inspection precision. These high-resolution sensors allow one to scan an environment and
store it in a 3D point cloud format for further processing and analysis by maintenance
specialists to prevent fires and damage to the electrical system. However, this task is
especially demanding to handle on time when we consider the extensive area that the
transmission network covers. Nonetheless, the transition to point cloud data allows us to
take advantage of Deep Learning to automate these inspections, by detecting collisions
between the grid and the revolving scene.
Deep Learning is a recent and powerful tool that has been successfully applied to a
myriad of real-life problems, such as image recognition and speech generation. With the
introduction of affordable LiDAR sensors, the application of Deep Learning on 3D data
emerged, with numerous methods being proposed every day to address difficult problems,
from 3D object detection to 3D point cloud segmentation. Alas, state-of-the-art methods
are remarkably complex, composed of millions of trainable parameters, and take several
weeks, if not months, to train on specific hardware, which makes it difficult for traditional
companies, like utilities, to employ them.
Therefore, we explore a novel mathematical framework that allows us to define tailored
operators that incorporate prior knowledge regarding our problem. These operators
are then integrated into a learning agent, called SCENE-Net, that detects power line supporting
towers in 3D point clouds. SCENE-Net allows for the interpretability of its results,
which is not possible in conventional models, it shows an efficient training and inference
time of 85 mn and 20 ms on a regular laptop. Our model is composed of 11 trainable
geometrical parameters, like the height of a cylinder, and has a Precision gain of 24%
against a comparable CNN with 2190 parameters.A inspeção e manutenção de redes de transmissão de energia são tarefas cruciais para
operadores de rede. Recentemente, foram adotadas inspeções utilizando sensores LiDAR
de forma a acelerar este processo e aumentar a sua precisão. Estes sensores são objetos de
alta precisão que conseguem inspecionar ambientes e guarda-los no formato de nuvens
de pontos 3D, para serem posteriormente analisadas por specialistas que procuram prevenir
fogos florestais e danos à estruta eléctrica. No entanto, esta tarefa torna-se bastante
difícil de concluir em tempo útil pois a rede de transmissão é bastasnte vasta. Por isso,
podemos tirar partido da transição para dados LiDAR e utilizar aprendizagem profunda
para automatizar as inspeções à rede.
Aprendizagem profunda é um campo recente e em grande desenvolvimento, sendo
aplicado a vários problemas do nosso quotidiano e facilmente atinge um desempenho
superior ao do ser humano, como em reconhecimento de imagens, geração de voz, entre
outros. Com o desenvolvimento de sensores LiDAR acessíveis, o uso de aprendizagem
profunda em dados 3D rapidamente se desenvolveu, apresentando várias metodologias
novas todos os dias que respondem a problemas complexos, como deteção de objetos
3D. No entanto, modelos do estado da arte são incrivelmente complexos e compostos
por milhões de parâmetros e demoram várias semanas, senão meses, a treinar em GPU
potentes, o que dificulta a sua utilização em empresas tradicionais, como a EDP.
Portanto, nós exploramos uma nova teoria matemática que nos permite definir operadores
específicos que incorporaram conhecimento sobre o nosso problema. Estes operadores
são integrados num modelo de aprendizagem prounda, designado SCENE-Net,
que deteta torres de suporte de linhas de transmissão em nuvens de pontos. SCENE-Net
permite a interpretação dos seus resultados, aspeto que não é possível com modelos convencionais,
demonstra um treino eficiente de 85 minutos e tempo de inferência de 20
milissegundos num computador tradicional. O nosso modelo contém apenas 11 parâmetros
geométricos, como a altura de um cilindro, e demonstra um ganho de Precisão de
24% quando comparado com uma CNN com 2190 parâmetros
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