Modelling the Latent Components of Personal Happiness

The interactions of users and items in recommender system could be naturally modeled as a user-item bipartite graph. In recent years, we have witnessed an emerging research effort in exploring user-item graph for collaborative filtering methods. Nevertheless, the formation of user-item interactions typically arises from highly complex latent purchasing motivations, such as high cost performance or eye-catching appearance, which are indistinguishably represented by the edges. The existing approaches still remain the differences between various purchasing motivations unexplored, rendering the inability to capture fine-grained user preference. Therefore, in this paper we propose a novel Multi-Component graph convolutional Collaborative Filtering (MCCF) approach to distinguish the latent purchasing motivations underneath the observed explicit user-item interactions. Specifically, there are two elaborately designed modules, decomposer and combiner, inside MCCF. The former first decomposes the edges in user-item graph to identify the latent components that may cause the purchasing relationship; the latter then recombines these latent components automatically to obtain unified embeddings for prediction. Furthermore, the sparse regularizer and weighted random sample strategy are utilized to alleviate the overfitting problem and accelerate the optimization. Empirical results on three real datasets and a synthetic dataset not only show the significant performance gains of MCCF, but also well demonstrate the necessity of considering multiple components.

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Detecting latent components in ordinal data with overdispersion by means of a mixture distribution

Quality & Quantity ◽

10.1007/s11135-014-0113-9 ◽

2014 ◽

Vol 49 (3) ◽

pp. 977-987 ◽

Cited By ~ 7

Author(s):

Maria Iannario

Keyword(s):

Ordinal Data ◽

Mixture Distribution ◽

Latent Components

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Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology

10.1101/637827 ◽

2019 ◽

Cited By ~ 5

Author(s):

Valeria Kebets ◽

Avram J. Holmes ◽

Csaba Orban ◽

Siyi Tang ◽

Jingwei Li ◽

...

Keyword(s):

Clinical Symptoms ◽

Brain Networks ◽

Higher Order ◽

Large Set ◽

Imaging Data ◽

Multiple Control ◽

Whole Brain ◽

Diagnostic Categories ◽

Intermediate Phenotypes ◽

Latent Components

AbstractBackgroundThere is considerable interest in a dimensional transdiagnostic approach to psychiatry. Most transdiagnostic studies have derived factors based only on clinical symptoms, which might miss possible links between psychopathology, cognitive processes and personality traits. Furthermore, many psychiatric studies focus on higher-order association brain networks, thus neglecting the potential influence of huge swaths of the brain.MethodsA multivariate data-driven approach (partial least squares; PLS) was utilized to identify latent components linking a large set of clinical, cognitive and personality measures to whole-brain resting-state functional connectivity (RSFC) patterns across 224 participants. The participants were either healthy (N=110) or diagnosed with bipolar disorder (N=40), attention-deficit/hyperactivity disorder (N=37), schizophrenia (N=29) or schizoaffective disorder (N=8). In contrast to traditional case-control analyses, the diagnostic categories were not utilized in the PLS analysis, but were helpful for interpreting the components.ResultsOur analyses revealed three latent components corresponding to general psychopathology, cognitive dysfunction and impulsivity. Each component was associated with a unique whole-brain RSFC signature and shared across all participants. The components were robust across multiple control analyses and replicated using independent task functional magnetic resonance imaging data from the same participants. Strikingly, all three components featured connectivity alterations within the somatosensory-motor network, and its connectivity with subcortical structures and cortical executive networks.ConclusionsWe identified three distinct dimensions with dissociable (but overlapping) whole-brain RSFC signatures across healthy individuals and individuals with psychiatric illness, providing potential intermediate phenotypes that span across diagnostic categories. Our results suggest expanding the focus of psychiatric neuroscience beyond higher-order brain networks.

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