5,870 research outputs found
Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling
Conversion rate (CVR) prediction is one of the most critical tasks for
digital display advertising. Commercial systems often require to update models
in an online learning manner to catch up with the evolving data distribution.
However, conversions usually do not happen immediately after a user click. This
may result in inaccurate labeling, which is called delayed feedback problem. In
previous studies, delayed feedback problem is handled either by waiting
positive label for a long period of time, or by consuming the negative sample
on its arrival and then insert a positive duplicate when a conversion happens
later. Indeed, there is a trade-off between waiting for more accurate labels
and utilizing fresh data, which is not considered in existing works. To strike
a balance in this trade-off, we propose Elapsed-Time Sampling Delayed Feedback
Model (ES-DFM), which models the relationship between the observed conversion
distribution and the true conversion distribution. Then we optimize the
expectation of true conversion distribution via importance sampling under the
elapsed-time sampling distribution. We further estimate the importance weight
for each instance, which is used as the weight of loss function in CVR
prediction. To demonstrate the effectiveness of ES-DFM, we conduct extensive
experiments on a public data and a private industrial dataset. Experimental
results confirm that our method consistently outperforms the previous
state-of-the-art results.Comment: This paper has been accepted by AAAI 202
Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems
Predicting conversion rate (e.g., the probability that a user will purchase
an item) is a fundamental problem in machine learning based recommender
systems. However, accurate conversion labels are revealed after a long delay,
which harms the timeliness of recommender systems. Previous literature
concentrates on utilizing early conversions to mitigate such a delayed feedback
problem. In this paper, we show that post-click user behaviors are also
informative to conversion rate prediction and can be used to improve
timeliness. We propose a generalized delayed feedback model (GDFM) that unifies
both post-click behaviors and early conversions as stochastic post-click
information, which could be utilized to train GDFM in a streaming manner
efficiently. Based on GDFM, we further establish a novel perspective that the
performance gap introduced by delayed feedback can be attributed to a temporal
gap and a sampling gap. Inspired by our analysis, we propose to measure the
quality of post-click information with a combination of temporal distance and
sample complexity. The training objective is re-weighted accordingly to
highlight informative and timely signals. We validate our analysis on public
datasets, and experimental performance confirms the effectiveness of our
method.Comment: NeurIPS'2
Learning Classifiers under Delayed Feedback with a Time Window Assumption
We consider training a binary classifier under delayed feedback (DF
Learning). In DF Learning, we first receive negative samples; subsequently,
some samples turn positive. This problem is conceivable in various real-world
applications such as online advertisements, where the user action takes place
long after the first click. Owing to the delayed feedback, simply separating
the positive and negative data causes a sample selection bias. One solution is
to assume that a long time window after first observing a sample reduces the
sample selection bias. However, existing studies report that only using a
portion of all samples based on the time window assumption yields suboptimal
performance, and the use of all samples along with the time window assumption
improves empirical performance. Extending these existing studies, we propose a
method with an unbiased and convex empirical risk constructed from the whole
samples under the time window assumption. We provide experimental results to
demonstrate the effectiveness of the proposed method using a real traffic log
dataset
Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks
The delayed feedback problem is one of the most pressing challenges in
predicting the conversion rate since users' conversions are always delayed in
online commercial systems. Although new data are beneficial for continuous
training, without complete feedback information, i.e., conversion labels,
training algorithms may suffer from overwhelming fake negatives. Existing
methods tend to use multitask learning or design data pipelines to solve the
delayed feedback problem. However, these methods have a trade-off between data
freshness and label accuracy. In this paper, we propose Delayed Feedback
Modeling by Dynamic Graph Neural Network (DGDFEM). It includes three stages,
i.e., preparing a data pipeline, building a dynamic graph, and training a CVR
prediction model. In the model training, we propose a novel graph convolutional
method named HLGCN, which leverages both high-pass and low-pass filters to deal
with conversion and non-conversion relationships. The proposed method achieves
both data freshness and label accuracy. We conduct extensive experiments on
three industry datasets, which validate the consistent superiority of our
method
Unbiased Delayed Feedback Label Correction for Conversion Rate Prediction
Conversion rate prediction is critical to many online applications such as
digital display advertising. To capture dynamic data distribution, industrial
systems often require retraining models on recent data daily or weekly.
However, the delay of conversion behavior usually leads to incorrect labeling,
which is called delayed feedback problem. Existing work may fail to introduce
the correct information about false negative samples due to data sparsity and
dynamic data distribution. To directly introduce the correct feedback label
information, we propose an Unbiased delayed feedback Label Correction framework
(ULC), which uses an auxiliary model to correct labels for observed negative
feedback samples. Firstly, we theoretically prove that the label-corrected loss
is an unbiased estimate of the oracle loss using true labels. Then, as there
are no ready training data for label correction, counterfactual labeling is
used to construct artificial training data. Furthermore, since counterfactual
labeling utilizes only partial training data, we design an embedding-based
alternative training method to enhance performance. Comparative experiments on
both public and private datasets and detailed analyses show that our proposed
approach effectively alleviates the delayed feedback problem and consistently
outperforms the previous state-of-the-art methods.Comment: accepted by KDD 202
Credit risk assessment: Evidence from banking industry
Measuring different risk factors such as credit risk in banking industry has been an interesting area of studies. The artificial neural network is a nonparametric method developed to succeed for measuring credit risk and this method is applied to measure the credit risk. This research’s neural network follows back propagation paradigm, which enables it to use historical data for predicting future values with very good out of sample fitting. Macroeconomic variables including GDP, exchange rate, inflation rate, stock price index, and M2 are used to forecast credit risk for two Iranian banks; namely Saderat and Sarmayeh over the period 2007-2011. Research data are being tested for ADF and Causality Granger tests before entering the ANN to achieve the best lag structure for the research model. MSE and R values for the developed ANN in this research respectively are 86×〖10〗^(-4) and 0.9885, respectively. The results showed that ANN was able to predict banks’ credit risk with low error. Sensibility analyses which has accomplished on this research’s ANN corroborates that M2 has the highest effect on the ANN’s credit risk and should be considered as an additional leading indicator by Iran’s banking authorities. These matters confirm validation of macroeconomic notions in Iran’s credit systematic risk
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