Hierarchical Hyperedge Embedding-Based Representation Learning for Group Recommendation

Group recommendation aims to recommend items to a group of users. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, where groups are formed ad hoc and users may just constitute a group for the first time—that is, the historical group-item interaction records are highly limited. Most state-of-the-art works have addressed the challenge by aggregating group members’ personal preferences to learn the group representation. However, the representation learning for a group is most complex beyond the aggregation or fusion of group member representation, as the personal preferences and group preferences may be in different spaces and even orthogonal. In addition, the learned user representation is not accurate due to the sparsity of users’ interaction data. Moreover, the group similarity in terms of common group members has been overlooked, which, however, has the great potential to improve the group representation learning. In this work, we focus on addressing the aforementioned challenges in the group representation learning task, and devise a hierarchical hyperedge embedding-based group recommender, namely HyperGroup. Specifically, we propose to leverage the user-user interactions to alleviate the sparsity issue of user-item interactions, and design a graph neural network-based representation learning network to enhance the learning of individuals’ preferences from their friends’ preferences, which provides a solid foundation for learning groups’ preferences. To exploit the group similarity (i.e., overlapping relationships among groups) to learn a more accurate group representation from highly limited group-item interactions, we connect all groups as a network of overlapping sets (a.k.a. hypergraph), and treat the task of group preference learning as embedding hyperedges (i.e., user sets/groups) in a hypergraph, where an inductive hyperedge embedding method is proposed. To further enhance the group-level preference modeling, we develop a joint training strategy to learn both user-item and group-item interactions in the same process. We conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority of our proposed HyperGroup in comparison to the state-of-the-art baselines.

Netralitas Aparatur Sipil Negara Dalam Pemilihan Umum Kepala Daerah Kabupaten Asahan Tahun 2020

This study aims to find out and analyze the neutrality of the State Civil Apparatus in the 2020 Asahan district head election as well as to describe and analyze what factors and obstacles to the non-neutrality of the state civil apparatus in the 2020 Asahan District Head General Election. The type of research used is descriptive qualitative with descriptive analytical method. This study uses a qualitative approach that produces descriptive data in the form of recording researchers and facts that will be found in the field. This study uses the theory of bureaucracy according to Weber (1974) and the theory of neutrality according to Amin which states that neutral means not taking sides and not being involved. The results of this study revealed a lack of neutrality for ASN in Asahan Regency as evidenced by violations involving 8 ASN and field facts based on observations and interviews. Meanwhile, the factors that lead to the occurrence of violations are the position factor and the group similarity factor. From the research that has been carried out, it was found that the application of this neutrality rule has obstacles in the form of obstacles from the ASN body itself, the suboptimal monitoring system carried out by Bawaslu and KASN, public understanding of ASN neutrality, and the absence of a budget for monitoring ASN neutrality in the PILKADA of the Asahan Regency Government.

CIDER: an interpretable meta-clustering framework for single-cell RNA-seq data integration and evaluation

Clustering of joint single-cell RNA-Seq (scRNA-Seq) data is often challenged by confounding factors, such as batch effects and biologically relevant variability. Existing batch effect removal methods typically require strong assumptions on the composition of cell populations being near identical across samples. Here, we present CIDER, a meta-clustering workflow based on inter-group similarity measures. We demonstrate that CIDER outperforms other scRNA-Seq clustering methods and integration approaches in both simulated and real datasets. Moreover, we show that CIDER can be used to assess the biological correctness of integration in real datasets, while it does not require the existence of prior cellular annotations.

An interpretable meta-clustering framework for single-cell RNA-Seq data integration and evaluation

Single-cell RNA sequencing (scRNA-Seq) datasets that are produced from clinical samples are often confounded by batch effects and inter-patient variability. Existing batch effect removal methods typically require strong assumptions on the composition of cell populations being near identical across patients. Here we present a novel meta-clustering workflow, CIDER, based on inter-group similarity measures. We demonstrate that CIDER outperforms other scRNA-Seq clustering methods and integration approaches in both simulated and real datasets. Moreover, we show that CIDER can be used to assess the biological correctness of integration in real datasets, while it does not require the existence of prior cellular annotations.

The specificity of lifestyle of the individuals with different type of life scenario

This article is dedicated to examination of the specificity of assessment of the individuals with different type of life scenario. Having compared the patterns of worldview and manifestations of life scenario of the individual, the author considers the latter as a component of in-depth structures of worldview, reflected on the level of semantic structures associated with the implementation of scenario in form of the specificity of assessment of respondents. One of the manifestations of life scenario of the individual in semantic structures is the specificity of assessment of their lifestyle. Since lifestyle represents a system of activities the person is engaged in, and life scenario by definition is related with saturation of subjective time with different ways of pastime, the process of realization of life scenario depends on the specifics of the content of lifestyle as a system of activities. The empirical research is conducted on the basis of specifically developed questionnaire; the three groups of respondents with different type of life scenario were formed. The respondents were offered to assess their lifestyle using a specialized semantic differential. The obtained data was processed via the method of semantic universals. The acquired results indicate that the descriptors included in the semantic universals of the assessment of lifestyle correspond to the type of life scenario of the individual. The groups of respondents with different type of life scenario demonstrate a various degree of in-group similarity in assessments of their lifestyle. Therefore, the specificity of lifestyle assessment is one of the manifestations of life scenario of the individual.

Personalized estimates of morphometric similarity in bipolar disorder and schizophrenia

Bipolar disorder and schizophrenia are associated with brain morphometry alterations. This study investigates inter-individual variability in brain structural profiles, both within diagnostic groups and between patients and healthy individuals. Brain morphometric measures from three independent samples of patients with schizophrenia (n = 168), bipolar disorder (n = 122), and healthy individuals (n = 180) were modeled as single vectors to generated individualized profiles of subcortical volumes and regional cortical thickness. These profiles were then used to compute a person-based similarity index (PBSI) for subcortical volumes and for regional cortical thickness, to quantify the within-group similarity of the morphometric profile of each individual to that of the other participants in the same diagnostic group. There was no effect of diagnosis on the PBSI for subcortical volumes. In contrast, compared to healthy individuals, the PBSI for cortical thickness was lower in patients with schizophrenia (effect size = 0.4, p ≤ 0.0002), but not in patients with bipolar disorder. The results were robust and reproducible across samples. We conclude that disease mechanisms for these disorders produce modest inter-individual variations in brain morphometry that should be considered in future studies attempting to cluster patients in subgroups.

The group matters: examining the effect of group characteristics in online brand communities

PurposeIn an online brand community (OBC), consumers as a group occupy a dominant position, and their horizontal interactions are pivotal for the consumer–brand relationship. However, little is known about the effect of group characteristics on brands. To partially fill this gap, this study examines how group characteristics influence brand loyalty from the perspective of social identity theory. Specifically, by collecting data from an online survey, this paper investigates the effects of group similarity, group receptivity and group involvement on brand loyalty, the mediating role of community identification and the moderating effect of tenure in a community.Design/methodology/approachData from 482 brand community members were analyzed using structural equation modeling.FindingsThe results show that group similarity, group receptivity and group involvement increase brand loyalty; furthermore, the effect is mediated by community identification. Moreover, group receptivity is more effective in fostering community identification of short-tenure members, whereas group similarity plays a more important role for long-tenure members.Originality/valueThis paper enriches the brand community literature from a group perspective and provides implications for how brand communities develop customer loyalty strategies.