A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions

Traditional recommendation systems are faced with two long-standing obstacles, namely, data sparsity and cold-start problems, which promote the emergence and development of Cross-Domain Recommendation (CDR). The core idea of CDR is to leverage information collected from other domains to alleviate the two problems in one domain. Over the last decade, many efforts have been engaged for cross-domain recommendation. Recently, with the development of deep learning and neural networks, a large number of methods have emerged. However, there is a limited number of systematic surveys on CDR, especially regarding the latest proposed methods as well as the recommendation scenarios and recommendation tasks they address. In this survey paper, we first proposed a two-level taxonomy of cross-domain recommendation which classifies different recommendation scenarios and recommendation tasks. We then introduce and summarize existing cross-domain recommendation approaches under different recommendation scenarios in a structured manner. We also organize datasets commonly used. We conclude this survey by providing several potential research directions about this field

Jointly Modeling Heterogeneous Student Behaviors and Interactions Among Multiple Prediction Tasks

Prediction tasks about students have practical significance for both student and college. Making multiple predictions about students is an important part of a smart campus. For instance, predicting whether a student will fail to graduate can alert the student affairs office to take predictive measures to help the student improve his/her academic performance. With the development of information technology in colleges, we can collect digital footprints which encode heterogeneous behaviors continuously. In this paper, we focus on modeling heterogeneous behaviors and making multiple predictions together, since some prediction tasks are related and learning the model for a specific task may have the data sparsity problem. To this end, we propose a variant of LSTM and a soft-attention mechanism. The proposed LSTM is able to learn the student profile-aware representation from heterogeneous behavior sequences. The proposed soft-attention mechanism can dynamically learn different importance degrees of different days for every student. In this way, heterogeneous behaviors can be well modeled. In order to model interactions among multiple prediction tasks, we propose a co-attention mechanism based unit. With the help of the stacked units, we can explicitly control the knowledge transfer among multiple tasks. We design three motivating behavior prediction tasks based on a real-world dataset collected from a college. Qualitative and quantitative experiments on the three prediction tasks have demonstrated the effectiveness of our model

Offline Memory Reprocessing: Involvement of the Brain's Default Network in Spontaneous Thought Processes

BACKGROUND: Spontaneous thought processes (STPs), also called daydreaming or mind-wandering, occur ubiquitously in daily life. However, the functional significance of STPs remains largely unknown. METHODOLOGY/PRINCIPAL FINDING: Using functional magnetic resonance imaging (fMRI), we first identified an STPs-network whose activity was positively correlated with the subjects' tendency of having STPs during a task-free state. The STPs-network was then found to be strongly associated with the default network, which has previously been established as being active during the task-free state. Interestingly, we found that offline reprocessing of previously memorized information further increased the activity of the STPs-network regions, although during a state with less STPs. In addition, we found that the STPs-network kept a dynamic balance between functional integration and functional separation among its component regions to execute offline memory reprocessing in STPs. CONCLUSION/SIGNIFICANCE: These findings strengthen a view that offline memory reprocessing and STPs share the brain's default network, and thus implicate that offline memory reprocessing may be a predetermined function of STPs. This supports the perspective that memory can be consolidated and modified during STPs, and thus gives rise to a dynamic behavior dependent on both previous external and internal experiences

An open science resource for establishing reliability and reproducibility in functional connectomics

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included

Handedness-Related Functional Connectivity Using Low-Frequency BOLD Fluctuations

Functional neuroimaging studies of handedness have traditionally focused on exploring regionally activated discrepancies, yet little is known regarding the effect of handedness on functional connections of brain regions. Here, we reported the relationship between cerebellar functional connectivity patterns and handedness by low-frequency blood oxygenation level-dependent fluctuations. Compared with the right-handers, the left-handers show higher cerebellar-prefrontal, cerebellar-parietal, and cerebellar-temporal functional connectivity, and lower cerebellar-limbic connectivity. Previous non-human primate studies have shown that these regions with known contributions to higher cognitive functions have afferent or efferent connections with the cerebellum. Our findings suggest that these observed low-frequency fluctuation correlations may reflect actually anatomic connections between the cerebellum and cerebral cortex, and the cerebellar involvement in higher function may be associated with handedness