27 research outputs found
Confidence Ranking for CTR Prediction
Model evolution and constant availability of data are two common phenomena in
large-scale real-world machine learning applications, e.g. ads and
recommendation systems. To adapt, the real-world system typically retrain with
all available data and online learn with recently available data to update the
models periodically with the goal of better serving performance. In this paper,
we propose a novel framework, named Confidence Ranking, which designs the
optimization objective as a ranking function with two different models. Our
confidence ranking loss allows direct optimization of the logits output for
different convex surrogate functions of metrics, e.g. AUC and Accuracy
depending on the target task and dataset. Armed with our proposed methods, our
experiments show that the introduction of confidence ranking loss can
outperform all baselines on the CTR prediction tasks of public and industrial
datasets. This framework has been deployed in the advertisement system of
JD.com to serve the main traffic in the fine-rank stage.Comment: Accepted by WWW202
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction
Click-through rate (CTR) prediction is of great importance in recommendation
systems and online advertising platforms. When served in industrial scenarios,
the user-generated data observed by the CTR model typically arrives as a
stream. Streaming data has the characteristic that the underlying distribution
drifts over time and may recur. This can lead to catastrophic forgetting if the
model simply adapts to new data distribution all the time. Also, it's
inefficient to relearn distribution that has been occurred. Due to memory
constraints and diversity of data distributions in large-scale industrial
applications, conventional strategies for catastrophic forgetting such as
replay, parameter isolation, and knowledge distillation are difficult to be
deployed. In this work, we design a novel drift-aware incremental learning
framework based on ensemble learning to address catastrophic forgetting in CTR
prediction. With explicit error-based drift detection on streaming data, the
framework further strengthens well-adapted ensembles and freezes ensembles that
do not match the input distribution avoiding catastrophic interference. Both
evaluations on offline experiments and A/B test shows that our method
outperforms all baselines considered.Comment: This work has been accepted by SIGIR2
MIC2005: The Sixth Metaheuristics International Conference??-1 Applying Permutation Distance in VNS for QAP
In this paper, a new concept distance called permutation distance is proposed and exploited in detail. We combine it with the hamming distance and propose a group of new neighborhood structures in VNS for QAP. Numerical tests running on the standard benchmark library QAPLIB show that this approach would dramatically improve the performance of VNS for QAP. 1
NDGGNET-A Node Independent Gate based Graph Neural Networks
Graph Neural Networks (GNNs) is an architecture for structural data, and has
been adopted in a mass of tasks and achieved fabulous results, such as link
prediction, node classification, graph classification and so on. Generally, for
a certain node in a given graph, a traditional GNN layer can be regarded as an
aggregation from one-hop neighbors, thus a set of stacked layers are able to
fetch and update node status within multi-hops. For nodes with sparse
connectivity, it is difficult to obtain enough information through a single GNN
layer as not only there are only few nodes directly connected to them but also
can not propagate the high-order neighbor information. However, as the number
of layer increases, the GNN model is prone to over-smooth for nodes with the
dense connectivity, which resulting in the decrease of accuracy. To tackle this
issue, in this thesis, we define a novel framework that allows the normal GNN
model to accommodate more layers. Specifically, a node-degree based gate is
employed to adjust weight of layers dynamically, that try to enhance the
information aggregation ability and reduce the probability of over-smoothing.
Experimental results show that our proposed model can effectively increase the
model depth and perform well on several datasets
The Beneficial Effects of Quercetin, Curcumin, and Resveratrol in Obesity
Over the past two decades, obesity has been one of the major public health concerns in most countries. In the search for new molecules that could be used for the treatment of obesity, good perspectives have been opened up for polyphenols, a class of natural bioactive phytochemicals. Experimental and limited clinical trial evidence supports that some polyphenols such as quercetin, curcumin, and resveratrol have potential benefit functions on obesity treatment. This brief review focuses on the main functions of the above-named polyphenols on adipose tissue. These polyphenols may play beneficial effects on adipose tissue under obese condition by alleviating intracellular oxidative stress, reducing chronic low-grade inflammation, inhibiting adipogenesis and lipogenesis, and suppressing the differentiation of preadipocytes to mature adipocytes
Tuning social interactions’ strength drives collective response to light intensity in schooling fish
International audienceSchooling fish heavily rely on visual cues to interact with neighbors and avoid obstacles. The availability of sensory information is influenced by environmental conditions and changes in the physical environment that can alter the sensory environment of the fish, which in turn affects individual and group movements. In this study, we combine experiments and data-driven modeling to investigate the impact of varying levels of light intensity on social interactions and collective behavior in rummy-nose tetra fish. The trajectories of single fish and groups of fish swimming in a tank under different lighting conditions were analyzed to quantify their movements and spatial distribution. Interaction functions between two individuals and the fish interaction with the tank wall were reconstructed and modeled for each light condition. Our results demonstrate that light intensity strongly modulates social interactions between fish and their reactions to obstacles, which then impact collective motion patterns that emerge at the group level