Review-Based Cross-Domain Recommendation via Hyperbolic Embedding and Hierarchy-Aware Domain Disentanglement

The issue of data sparsity poses a significant challenge to recommender systems. In response to this, algorithms that leverage side information such as review texts have been proposed. Furthermore, Cross-Domain Recommendation (CDR), which captures domain-shareable knowledge and transfers it from a richer domain (source) to a sparser one (target), has received notable attention. Nevertheless, the majority of existing methodologies assume a Euclidean embedding space, encountering difficulties in accurately representing richer text information and managing complex interactions between users and items. This paper advocates a hyperbolic CDR approach based on review texts for modeling user-item relationships. We first emphasize that conventional distance-based domain alignment techniques may cause problems because small modifications in hyperbolic geometry result in magnified perturbations, ultimately leading to the collapse of hierarchical structures. To address this challenge, we propose hierarchy-aware embedding and domain alignment schemes that adjust the scale to extract domain-shareable information without disrupting structural forms. The process involves the initial embedding of review texts in hyperbolic space, followed by feature extraction incorporating degree-based normalization and structure alignment. We conducted extensive experiments to substantiate the efficiency, robustness, and scalability of our proposed model in comparison to state-of-the-art baselines

Perturb Initial Features: Generalization of Neural Networks Under Sparse Features for Semi-supervised Node Classification

Graph neural networks (GNNs) are commonly used in semi-supervised settings. Previous research has primarily focused on finding appropriate graph filters (e.g. aggregation methods) to perform well on both homophilic and heterophilic graphs. While these methods are effective, they can still suffer from the sparsity of node features, where the initial data contain few non-zero elements. This can lead to overfitting in certain dimensions in the first projection matrix, as training samples may not cover the entire range of graph filters (hyperplanes). To address this, we propose a novel data augmentation strategy. Specifically, by flipping both the initial features and hyperplane, we create additional space for training, which leads to more precise updates of the learnable parameters and improved robustness for unseen features during inference. To the best of our knowledge, this is the first attempt to mitigate the overfitting caused by the initial features. Extensive experiments on real-world datasets show that our proposed technique increases node classification accuracy by up to 46.5% relatively

Is Signed Message Essential for Graph Neural Networks?

Message-passing Graph Neural Networks (GNNs), which collect information from adjacent nodes, achieve satisfying results on homophilic graphs. However, their performances are dismal in heterophilous graphs, and many researchers have proposed a plethora of schemes to solve this problem. Especially, flipping the sign of edges is rooted in a strong theoretical foundation, and attains significant performance enhancements. Nonetheless, previous analyses assume a binary class scenario and they may suffer from confined applicability. This paper extends the prior understandings to multi-class scenarios and points out two drawbacks: (1) the sign of multi-hop neighbors depends on the message propagation paths and may incur inconsistency, (2) it also increases the prediction uncertainty (e.g., conflict evidence) which can impede the stability of the algorithm. Based on the theoretical understanding, we introduce a novel strategy that is applicable to multi-class graphs. The proposed scheme combines confidence calibration to secure robustness while reducing uncertainty. We show the efficacy of our theorem through extensive experiments on six benchmark graph datasets

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

Tele-Operated Lunar Rover Navigation Using Lidar

Near real-time tele-operated driving on the lunar surface remains constrained by bandwidth and signal latency despite the Moon s relative proximity. As part of our work within NASA s Human-Robotic Systems Project (HRS), we have developed a stand-alone modular LIDAR based safeguarded tele-operation system of hardware, middleware, navigation software and user interface. The system has been installed and tested on two distinct NASA rovers-JSC s Centaur2 lunar rover prototype and ARC s KRex research rover- and tested over several kilometers of tele-operated driving at average sustained speeds of 0.15 - 0.25 m/s around rocks, slopes and simulated lunar craters using a deliberately constrained telemetry link. The navigation system builds onboard terrain and hazard maps, returning highest priority sections to the off-board operator as permitted by bandwidth availability. It also analyzes hazard maps onboard and can stop the vehicle prior to contacting hazards. It is robust to severe pose errors and uses a novel scan alignment algorithm to compensate for attitude and elevation errors

Medios de Comunicación en Internet móvil: La televisión como modelo aún pendiente de éxito

La televisión en el móvil no ha acabado de arrancar comercialmente en la mayor parte de los países europeos. No existe una única razón que explique esta situación. Antes bien, cabe referirse a un conjunto de causas complejas (e interrelacionadas). En el lado de la oferta, deben analizarse aspectos técnicos (infraestructuras, estándares), económicos (modelos de negocio, disponibilidad de contenido) y normativos. En el lado de la demanda, es necesario investigar cuál es la utilidad que el servicio realmente (y no teóricamente) ofrece al consumidor y las circunstancias en que lo usaría. El estudio de todos estos factores es el objetivo de este artículo. Se concluye que la televisión móvil asentará su presencia en el mercado sólo si se sortean algunos de los obstáculos descritos, se pone el foco en el posible usuario (y no en el producto) y se cuenta con cierto apoyo institucional

Understanding of the Fintech Phenomenon in the Beholder???s Eyes in South Korea

Advances in information technology (IT) bring about technological innovation in financial businesses, referred to as financial technology (fintech), beyond the traditional financial industry. While fintech implies more convenient and various financial services to customers, it leads to more complexity in the financial sector, as different industry players (e.g., IT firms) can participate in financial businesses. The complexity of fintech causes controversial issues related to policies and the appropriate development direction. In order to provide insight into the current state of fintech, this study explores the fundamental understanding of the fintech phenomenon from the perspective of the major stakeholders (i.e., financial authorities, financial companies, IT firms) in South Korea. This study analyzed news articles, where those stakeholders expressed their arguments, by using a content analysis. The study also conducted an intensive examination of their arguments by using a core???periphery approach of social representations. This study found that while the three beholders had a common opinion on deregulation of the fintech industry, each of them had different knowledge of the phenomenon. By revealing each beholder???s structure of representations of fintech, this study not only provides common knowledge regarding fintech but also explicates the perceptual gaps among stakeholders. Findings of the study offer a big picture of current fintech initiatives, which can be useful knowledge for future research on fintech

The effects of second-screen viewing and the goal congruency of supplementary content on user perceptions

Users??? desire to view additional content while watching TV has increased significantly in recent years. This study investigates the optimal way to show supplementary content and evaluates the types of additional information that should be provided. We conduct two 2 (single screen vs. second screen) ?? 2 (providing additional content with congruent goals vs. incongruent goals) between-subjects design experiments, one with news information (utilitarian content) and the other with soft drama programming (hedonic content). The results showed that viewing supplemental content with related information on a different screen strengthens user perceptions of both news and drama. However, the interaction effect of second-screen viewing and the goals of additional content differed across the main types of TV content. The results of this study are relevant to both scholars and practitioners who seek ways to enhance the effectiveness of second-screen usage.clos

Users' Cognitive and Affective Response to the Risk to Privacy from a Smart Speaker

Smart speakers, which provide continuous real-time information and convenient services, increasingly permeate into users' homes. On the other hand, the environments of the smart speaker also cause privacy issues for the users by always listening to users' voices in their private homes. The privacy-prone environments of the smart speakers lead to the users' coping behaviors against privacy threats not only by increasing the users' privacy concerns but also by creating negative emotions. However, prior studies have predominantly focussed on cognitive frameworks and overlooked the impact of effect on users' coping behaviors in the context of privacy threats. Drawing on the stimuli-organisms-responses(S-O-R) framework, this study examines the mediating role of three representative negative emotions (anger, anxiety, disappointment) between users' privacy concerns and behaviors. The results indicate that the relationships between privacy concerns, negative emotions, and various privacy behaviors in the context of the smart speaker, emphasizing the mediating role of negative emotions. Findings of the study can enhance our knowledge of privacy research by adding the influence of effect in the cognitive-dominant framework

Individual Differences in Online Privacy Concern

We examined the effects of socio-demographics and personality traits on online privacy concern. We investigated this relationship in general and across different Internet functions. Using a large, diverse, population-representative sample (N = 5,242), we found that females, educated, and wealthier individuals tend to be concerned with online privacy to a greater extent. Among personality traits, agreeableness and conscientiousness were generally associated with an increased probability of being concerned with online privacy. These results imply that socio-demographics and personality traits provide explanatory insights into online privacy concern