1,255 research outputs found
Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction
Extracting users' interests from their lifelong behavior sequence is crucial
for predicting Click-Through Rate (CTR). Most current methods employ a
two-stage process for efficiency: they first select historical behaviors
related to the candidate item and then deduce the user's interest from this
narrowed-down behavior sub-sequence. This two-stage paradigm, though effective,
leads to information loss. Solely using users' lifelong click behaviors doesn't
provide a complete picture of their interests, leading to suboptimal
performance. In our research, we introduce the Deep Group Interest Network
(DGIN), an end-to-end method to model the user's entire behavior history. This
includes all post-registration actions, such as clicks, cart additions,
purchases, and more, providing a nuanced user understanding. We start by
grouping the full range of behaviors using a relevant key (like item_id) to
enhance efficiency. This process reduces the behavior length significantly,
from O(10^4) to O(10^2). To mitigate the potential loss of information due to
grouping, we incorporate two categories of group attributes. Within each group,
we calculate statistical information on various heterogeneous behaviors (like
behavior counts) and employ self-attention mechanisms to highlight unique
behavior characteristics (like behavior type). Based on this reorganized
behavior data, the user's interests are derived using the Transformer
technique. Additionally, we identify a subset of behaviors that share the same
item_id with the candidate item from the lifelong behavior sequence. The
insights from this subset reveal the user's decision-making process related to
the candidate item, improving prediction accuracy. Our comprehensive
evaluation, both on industrial and public datasets, validates DGIN's efficacy
and efficiency
DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation
Point-of-Interest (POI) recommendation plays a vital role in various
location-aware services. It has been observed that POI recommendation is driven
by both sequential and geographical influences. However, since there is no
annotated label of the dominant influence during recommendation, existing
methods tend to entangle these two influences, which may lead to sub-optimal
recommendation performance and poor interpretability. In this paper, we address
the above challenge by proposing DisenPOI, a novel Disentangled dual-graph
framework for POI recommendation, which jointly utilizes sequential and
geographical relationships on two separate graphs and disentangles the two
influences with self-supervision. The key novelty of our model compared with
existing approaches is to extract disentangled representations of both
sequential and geographical influences with contrastive learning. To be
specific, we construct a geographical graph and a sequential graph based on the
check-in sequence of a user. We tailor their propagation schemes to become
sequence-/geo-aware to better capture the corresponding influences. Preference
proxies are extracted from check-in sequence as pseudo labels for the two
influences, which supervise the disentanglement via a contrastive loss.
Extensive experiments on three datasets demonstrate the superiority of the
proposed model.Comment: Accepted by ACM International Conference on Web Search and Data
Mining (WSDM'23
Chinese version of Dominic Interactive â A self-report video game for assessing mental health in young children
ObjectivesAssess the validity of the Chinese version of the Dominic Interactive (DI), a 91-item, video-based diagnostic screening instrument for children that assesses four internalized disorders (phobias, separation anxiety disorder, generalized anxiety disorder, and major depressive disorder) and three externalized disorders (attention-deficit/hyperactivity disorder, conduct disorder, oppositional defiant disorder).Methods(1) Compare DI-generated âprobableâ or âpossibleâ diagnoses to diagnoses based on the Development and Well-Being Assessment (DAWBA) instrument in 113 psychiatric outpatients and 20 community controls. (2) Administer DI to 1,479 children from elementary schools in Tianjin.ResultsIn the validation sample, DI with DAWBA concordance was much greater for internalized disorders (mean Kappaâ=â0.56) than for externalized disorders (mean kappaâ=â0.11). The positive predictive value of DI diagnoses ranged from 0.96 (generalized anxiety disorder) to 25% (oppositional defiant disorder) and negative from 0.81 to 0.96. Using âprobableâ cuts provides better results. In the survey, prevalence of probable DI disorders ranged from 1.0% (conduct disorder) to 13.1% (phobias). Internal consistency of all DI items was excellent (Cronbach alphaâ=â0.93) and that of the seven subscales ranged from 0.64 (phobias) to 0.87 (major depressive disorder). In multilevel SEM analyses, SRMR (Standardized root mean square residual) or each of the seven diagnoses was below 0.08 and each coefficient of determination was below 0.60.ConclusionThe Chinese DI is a convenient method of screening common mental disorders in Chinese children mainly for internalized disorders, which are the most prevalent diagnoses in that population. However its high negative predictive values for externalized could be used for screening
Regulatory Network and Prognostic Effect Investigation of PIP4K2A in Leukemia and Solid Cancers
Germline variants of PIP4K2A impact susceptibility of acute lymphoblastic leukemia (ALL) through inducing its overexpression. Although limited reports suggested the oncogenic role of PIP4K2A in cancers, regulatory network and prognostic effect of this gene remains poorly understood in tumorigenesis and leukemogenesis. In this study, we conducted genome-wide gene expression association analyses in pediatric B-ALL cohorts to discover expression associated genes and pathways, which is followed by the bioinformatics analyses to investigate the prognostic role of PIP4K2A and its related genes in multiple cancer types. 214 candidates were identified to be significantly associated with PIP4K2A expression in ALL patients, with known cancer-related genes rankings the top (e.g., RAC2, RBL2, and TFDP1). These candidates do not only tend to be clustered in the same types of leukemia, but can also separate the patients into novel molecular subtypes. PIP4K2A is noticed to be frequently overexpressed in multiple other types of leukemia and solid cancers from cancer cohorts including TCGA, and associated with its candidates in subtype-specific and cancer-specific manners. Interestingly, the association status varied in tumors compared to their matched normal tissues. Moreover, PIP4K2A and its related candidates exhibit stage-independent prognostic effects in multiple cancers, mostly with its lower expression significantly associated with longer overall survival (p < 0.05). Our findings reveal the transcriptional regulatory network of PIP4K2A in leukemia, and suggest its potentially important role on molecular subtypes of multiple cancers and subsequent treatment outcomes
Search for light dark matter from atmosphere in PandaX-4T
We report a search for light dark matter produced through the cascading decay
of mesons, which are created as a result of inelastic collisions between
cosmic rays and Earth's atmosphere. We introduce a new and general framework,
publicly accessible, designed to address boosted dark matter specifically, with
which a full and dedicated simulation including both elastic and quasi-elastic
processes of Earth attenuation effect on the dark matter particles arriving at
the detector is performed. In the PandaX-4T commissioning data of 0.63
tonneyear exposure, no significant excess over background is observed.
The first constraints on the interaction between light dark matter generated in
the atmosphere and nucleus through a light scalar mediator are obtained. The
lowest excluded cross-section is set at for
dark matter mass of MeV and mediator mass of 300 MeV. The
lowest upper limit of to dark matter decay branching ratio is
A Search for Light Fermionic Dark Matter Absorption on Electrons in PandaX-4T
We report a search on a sub-MeV fermionic dark matter absorbed by electrons
with an outgoing active neutrino using the 0.63 tonne-year exposure collected
by PandaX-4T liquid xenon experiment. No significant signals are observed over
the expected background. The data are interpreted into limits to the effective
couplings between such dark matter and electrons. For axial-vector or vector
interactions, our sensitivity is competitive in comparison to existing
astrophysical bounds on the decay of such dark matter into photon final states.
In particular, we present the first direct detection limits for an axial-vector
(vector) interaction which are the strongest in the mass range from 25 to 45
(35 to 50) keV/c
Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at âs = 13 TeV
Abstract The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (dÌ t) and chromomagnetic (ÎŒÌ t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fbâ1. The linearized variable AFB(1) is used to approximate the asymmetry. Candidate t t ÂŻ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for t t ÂŻ final states. The values found for the parameters are AFB(1)=0.048â0.087+0.095(stat)â0.029+0.020(syst),ÎŒÌt=â0.024â0.009+0.013(stat)â0.011+0.016(syst), and a limit is placed on the magnitude of | dÌ t| < 0.03 at 95% confidence level. [Figure not available: see fulltext.
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